NIPS 2017 paper 下载链接

来源： https://papers.nips.cc/book/advances-in-neural-information-processing-systems-30-2017

• Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning: https://papers.nips.cc/paper/6606-wider-and-deeper-cheaper-and-faster-tensorized-lstms-for-sequence-learning
• Concentration of Multilinear Functions of the Ising Model with Applications to Network Data: https://papers.nips.cc/paper/6607-concentration-of-multilinear-functions-of-the-ising-model-with-applications-to-network-data
• Deep Subspace Clustering Network: https://papers.nips.cc/paper/6608-deep-subspace-clustering-network
• Attentional Pooling for Action Recognition: https://papers.nips.cc/paper/6609-attentional-pooling-for-action-recognition
• On the Consistency of Quick Shift: https://papers.nips.cc/paper/6610-on-the-consistency-of-quick-shift
• Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization: https://papers.nips.cc/paper/6611-breaking-the-nonsmooth-barrier-a-scalable-parallel-method-for-composite-optimization
• Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis: https://papers.nips.cc/paper/6612-dual-agent-gans-for-photorealistic-and-identity-preserving-profile-face-synthesis
• Dilated Recurrent Neural Networks: https://papers.nips.cc/paper/6613-dilated-recurrent-neural-networks
• Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs: https://papers.nips.cc/paper/6614-hunt-for-the-unique-stable-sparse-and-fast-feature-learning-on-graphs
• Scalable Generalized Linear Bandits: Online Computation and Hashing: https://papers.nips.cc/paper/6615-scalable-generalized-linear-bandits-online-computation-and-hashing
• Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models: https://papers.nips.cc/paper/6616-probabilistic-models-for-integration-error-in-the-assessment-of-functional-cardiac-models
• Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent: https://papers.nips.cc/paper/6617-machine-learning-with-adversaries-byzantine-tolerant-gradient-descent
• Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning: https://papers.nips.cc/paper/6618-dynamic-safe-interruptibility-for-decentralized-multi-agent-reinforcement-learning
• Interactive Submodular Bandit: https://papers.nips.cc/paper/6619-interactive-submodular-bandit
• Scene Physics Acquisition via Visual De-animation: https://papers.nips.cc/paper/6620-scene-physics-acquisition-via-visual-de-animation
• Label Efficient Learning of Transferable Representations acrosss Domains and Tasks: https://papers.nips.cc/paper/6621-label-efficient-learning-of-transferable-representations-acrosss-domains-and-tasks
• Decoding with Value Networks for Neural Machine Translation: https://papers.nips.cc/paper/6622-decoding-with-value-networks-for-neural-machine-translation
• Parametric Simplex Method for Sparse Learning: https://papers.nips.cc/paper/6623-parametric-simplex-method-for-sparse-learning
• Group Sparse Additive Machine: https://papers.nips.cc/paper/6624-group-sparse-additive-machine
• Uprooting and Rerooting Higher-order Graphical Models: https://papers.nips.cc/paper/6625-uprooting-and-rerooting-higher-order-graphical-models
• The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings: https://papers.nips.cc/paper/6626-the-unreasonable-effectiveness-of-structured-random-orthogonal-embeddings
• From Parity to Preference: Learning with Cost-effective Notions of Fairness: https://papers.nips.cc/paper/6627-from-parity-to-preference-learning-with-cost-effective-notions-of-fairness
• Inferring Generative Model Structure with Static Analysis: https://papers.nips.cc/paper/6628-inferring-generative-model-structure-with-static-analysis
• Structured Embedding Models for Grouped Data: https://papers.nips.cc/paper/6629-structured-embedding-models-for-grouped-data
• A Linear-Time Kernel Goodness-of-Fit Test: https://papers.nips.cc/paper/6630-a-linear-time-kernel-goodness-of-fit-test
• Cortical microcircuits as gated-recurrent neural networks: https://papers.nips.cc/paper/6631-cortical-microcircuits-as-gated-recurrent-neural-networks
• k-Support and Ordered Weighted Sparsity for Overlapping Groups: Hardness and Algorithms: https://papers.nips.cc/paper/6632-k-support-and-ordered-weighted-sparsity-for-overlapping-groups-hardness-and-algorithms
• A simple model of recognition and recall memory: https://papers.nips.cc/paper/6633-a-simple-model-of-recognition-and-recall-memory
• On Structured Prediction Theory with Calibrated Convex Surrogate Losses: https://papers.nips.cc/paper/6634-on-structured-prediction-theory-with-calibrated-convex-surrogate-losses
• Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model: https://papers.nips.cc/paper/6635-best-of-both-worlds-transferring-knowledge-from-discriminative-learning-to-a-generative-visual-dialog-model
• MaskRNN: Instance Level Video Object Segmentation: https://papers.nips.cc/paper/6636-maskrnn-instance-level-video-object-segmentation
• Gated Recurrent Convolution Neural Network for OCR: https://papers.nips.cc/paper/6637-gated-recurrent-convolution-neural-network-for-ocr
• Towards Accurate Binary Convolutional Neural Network: https://papers.nips.cc/paper/6638-towards-accurate-binary-convolutional-neural-network
• Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks: https://papers.nips.cc/paper/6639-semi-supervised-learning-for-optical-flow-with-generative-adversarial-networks
• Learning a Multi-View Stereo Machine: https://papers.nips.cc/paper/6640-learning-a-multi-view-stereo-machine
• Phase Transitions in the Pooled Data Problem: https://papers.nips.cc/paper/6641-phase-transitions-in-the-pooled-data-problem
• Universal Style Transfer via Feature Transforms: https://papers.nips.cc/paper/6642-universal-style-transfer-via-feature-transforms
• On the Model Shrinkage Effect of Gamma Process Edge Partition Models: https://papers.nips.cc/paper/6643-on-the-model-shrinkage-effect-of-gamma-process-edge-partition-models
• Pose Guided Person Image Generation: https://papers.nips.cc/paper/6644-pose-guided-person-image-generation
• Inference in Graphical Models via Semidefinite Programming Hierarchies: https://papers.nips.cc/paper/6645-inference-in-graphical-models-via-semidefinite-programming-hierarchies
• Variable Importance Using Decision Trees: https://papers.nips.cc/paper/6646-variable-importance-using-decision-trees
• Preventing Gradient Explosions in Gated Recurrent Units: https://papers.nips.cc/paper/6647-preventing-gradient-explosions-in-gated-recurrent-units
• On the Power of Truncated SVD for General High-rank Matrix Estimation Problems: https://papers.nips.cc/paper/6648-on-the-power-of-truncated-svd-for-general-high-rank-matrix-estimation-problems
• f-GANs in an Information Geometric Nutshell: https://papers.nips.cc/paper/6649-f-gans-in-an-information-geometric-nutshell
• Multimodal Image-to-Image Translation by Enforcing Bi-Cycle Consistency: https://papers.nips.cc/paper/6650-multimodal-image-to-image-translation-by-enforcing-bi-cycle-consistency
• Mixture-Rank Matrix Approximation for Collaborative Filtering: https://papers.nips.cc/paper/6651-mixture-rank-matrix-approximation-for-collaborative-filtering
• Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms: https://papers.nips.cc/paper/6652-non-monotone-continuous-dr-submodular-maximization-structure-and-algorithms
• Learning with Average Top-k Loss: https://papers.nips.cc/paper/6653-learning-with-average-top-k-loss
• Learning multiple visual domains with residual adapters: https://papers.nips.cc/paper/6654-learning-multiple-visual-domains-with-residual-adapters
• Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions: https://papers.nips.cc/paper/6655-dykstras-algorithm-admm-and-coordinate-descent-connections-insights-and-extensions
• Flat2Sphere: Learning Spherical Convolution for Fast Features from 360° Imagery: https://papers.nips.cc/paper/6656-flat2sphere-learning-spherical-convolution-for-fast-features-from-360-imagery
• 3D Shape Reconstruction by Modeling 2.5D Sketch: https://papers.nips.cc/paper/6657-3d-shape-reconstruction-by-modeling-25d-sketch
• Multimodal Learning and Reasoning for Visual Question Answering: https://papers.nips.cc/paper/6658-multimodal-learning-and-reasoning-for-visual-question-answering
• Adversarial Surrogate Losses for Ordinal Regression: https://papers.nips.cc/paper/6659-adversarial-surrogate-losses-for-ordinal-regression
• Hypothesis Transfer Learning via Transformation Functions: https://papers.nips.cc/paper/6660-hypothesis-transfer-learning-via-transformation-functions
• Adversarial Invariant Feature Learning: https://papers.nips.cc/paper/6661-adversarial-invariant-feature-learning
• Convergence Analysis of Two-layer Neural Networks with ReLU Activation: https://papers.nips.cc/paper/6662-convergence-analysis-of-two-layer-neural-networks-with-relu-activation
• Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization: https://papers.nips.cc/paper/6663-doubly-accelerated-stochastic-variance-reduced-dual-averaging-method-for-regularized-empirical-risk-minimization
• Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks: https://papers.nips.cc/paper/6664-langevin-dynamics-with-continuous-tempering-for-training-deep-neural-networks
• Efficient Online Linear Optimization with Approximation Algorithms: https://papers.nips.cc/paper/6665-efficient-online-linear-optimization-with-approximation-algorithms
• Geometric Descent Method for Convex Composite Minimization: https://papers.nips.cc/paper/6666-geometric-descent-method-for-convex-composite-minimization
• Diffusion Approximations for Online Principal Component Estimation and Global Convergence: https://papers.nips.cc/paper/6667-diffusion-approximations-for-online-principal-component-estimation-and-global-convergence
• Avoiding Discrimination through Causal Reasoning: https://papers.nips.cc/paper/6668-avoiding-discrimination-through-causal-reasoning
• Nonparametric Online Regression while Learning the Metric: https://papers.nips.cc/paper/6669-nonparametric-online-regression-while-learning-the-metric
• Recycling for Fairness: Learning with Conditional Distribution Matching Constraints: https://papers.nips.cc/paper/6670-recycling-for-fairness-learning-with-conditional-distribution-matching-constraints
• Safe and Nested Subgame Solving for Imperfect-Information Games: https://papers.nips.cc/paper/6671-safe-and-nested-subgame-solving-for-imperfect-information-games
• Unsupervised Image-to-Image Translation Networks: https://papers.nips.cc/paper/6672-unsupervised-image-to-image-translation-networks
• Coded Distributed Computing for Inverse Problems: https://papers.nips.cc/paper/6673-coded-distributed-computing-for-inverse-problems
• A Screening Rule for l1-Regularized Ising Model Estimation: https://papers.nips.cc/paper/6674-a-screening-rule-for-l1-regularized-ising-model-estimation
• Improved Dynamic Regret for Non-degeneracy Functions: https://papers.nips.cc/paper/6675-improved-dynamic-regret-for-non-degeneracy-functions
• Learning Efficient Object Detection Models with Knowledge Distillation: https://papers.nips.cc/paper/6676-learning-efficient-object-detection-models-with-knowledge-distillation
• One-Sided Unsupervised Domain Mapping: https://papers.nips.cc/paper/6677-one-sided-unsupervised-domain-mapping
• Deep Mean-Shift Priors for Image Restoration: https://papers.nips.cc/paper/6678-deep-mean-shift-priors-for-image-restoration
• Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees: https://papers.nips.cc/paper/6679-greedy-algorithms-for-cone-constrained-optimization-with-convergence-guarantees
• A New Theory for Nonconvex Matrix Completion: https://papers.nips.cc/paper/6680-a-new-theory-for-nonconvex-matrix-completion
• Robust Hypothesis Test for Functional Effect with Gaussian Processes: https://papers.nips.cc/paper/6681-robust-hypothesis-test-for-functional-effect-with-gaussian-processes
• Lower bounds on the robustness to adversarial perturbations: https://papers.nips.cc/paper/6682-lower-bounds-on-the-robustness-to-adversarial-perturbations
• Minimizing a Submodular Function from Samples: https://papers.nips.cc/paper/6683-minimizing-a-submodular-function-from-samples
• Introspective Classification with Convolutional Nets: https://papers.nips.cc/paper/6684-introspective-classification-with-convolutional-nets
• Label Distribution Learning Forests: https://papers.nips.cc/paper/6685-label-distribution-learning-forests
• Unsupervised object learning from dense equivariant image labelling: https://papers.nips.cc/paper/6686-unsupervised-object-learning-from-dense-equivariant-image-labelling
• Compression-aware Training of Deep Neural Networks: https://papers.nips.cc/paper/6687-compression-aware-training-of-deep-neural-networks
• Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces: https://papers.nips.cc/paper/6688-multiscale-semi-markov-dynamics-for-intracortical-brain-computer-interfaces
• PredRNN: Recurrent Neural Networks for Video Prediction using Spatiotemporal LSTMs: https://papers.nips.cc/paper/6689-predrnn-recurrent-neural-networks-for-video-prediction-using-spatiotemporal-lstms
• Detrended Partial Cross Correlation for Brain Connectivity Analysis: https://papers.nips.cc/paper/6690-detrended-partial-cross-correlation-for-brain-connectivity-analysis
• Contrastive Learning for Image Captioning: https://papers.nips.cc/paper/6691-contrastive-learning-for-image-captioning
• Safe Model-based Reinforcement Learning with Stability Guarantees: https://papers.nips.cc/paper/6692-safe-model-based-reinforcement-learning-with-stability-guarantees
• Online multiclass boosting: https://papers.nips.cc/paper/6693-online-multiclass-boosting
• Matching on Balanced Nonlinear Representations for Treatment Effects Estimation: https://papers.nips.cc/paper/6694-matching-on-balanced-nonlinear-representations-for-treatment-effects-estimation
• Learning Overcomplete HMMs: https://papers.nips.cc/paper/6695-learning-overcomplete-hmms
• GP CaKe: Effective brain connectivity with causal kernels: https://papers.nips.cc/paper/6696-gp-cake-effective-brain-connectivity-with-causal-kernels
• Decoupling "when to update" from "how to update": https://papers.nips.cc/paper/6697-decoupling-when-to-update-from-how-to-update
• Self-Normalizing Neural Networks: https://papers.nips.cc/paper/6698-self-normalizing-neural-networks
• Learning to Pivot with Adversarial Networks: https://papers.nips.cc/paper/6699-learning-to-pivot-with-adversarial-networks
• MolecuLeNet: A continuous-filter convolutional neural network for modeling quantum interactions: https://papers.nips.cc/paper/6700-moleculenet-a-continuous-filter-convolutional-neural-network-for-modeling-quantum-interactions
• Active Bias: Training a More Accurate Neural Network by Emphasizing High Variance Samples: https://papers.nips.cc/paper/6701-active-bias-training-a-more-accurate-neural-network-by-emphasizing-high-variance-samples
• Differentiable Learning of Submodular Functions: https://papers.nips.cc/paper/6702-differentiable-learning-of-submodular-functions
• Inductive Representation Learning on Large Graphs: https://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs
• Subset Selection for Sequential Data: https://papers.nips.cc/paper/6704-subset-selection-for-sequential-data
• Question Asking as Program Generation: https://papers.nips.cc/paper/6705-question-asking-as-program-generation
• Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces: https://papers.nips.cc/paper/6706-revisiting-perceptron-efficient-and-label-optimal-learning-of-halfspaces
• Gradient Descent Can Take Exponential Time to Escape Saddle Points: https://papers.nips.cc/paper/6707-gradient-descent-can-take-exponential-time-to-escape-saddle-points
• Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction: https://papers.nips.cc/paper/6708-union-of-intersections-uoi-for-interpretable-data-driven-discovery-and-prediction
• One-Shot Imitation Learning: https://papers.nips.cc/paper/6709-one-shot-imitation-learning
• Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding: https://papers.nips.cc/paper/6710-learning-the-morphology-of-brain-signals-using-alpha-stable-convolutional-sparse-coding
• Integration Methods and Optimization Algorithms: https://papers.nips.cc/paper/6711-integration-methods-and-optimization-algorithms
• Sharpness, Restart and Acceleration: https://papers.nips.cc/paper/6712-sharpness-restart-and-acceleration
• Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition: https://papers.nips.cc/paper/6713-learning-koopman-invariant-subspaces-for-dynamic-mode-decomposition
• Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations: https://papers.nips.cc/paper/6714-soft-to-hard-vector-quantization-for-end-to-end-learning-compressible-representations
• Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data: https://papers.nips.cc/paper/6715-learning-spatiotemporal-piecewise-geodesic-trajectories-from-longitudinal-manifold-valued-data
• Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications: https://papers.nips.cc/paper/6716-improving-regret-bounds-for-combinatorial-semi-bandits-with-probabilistically-triggered-arms-and-its-applications
• Predictive-State Decoders: Encoding the Future into Recurrent Networks: https://papers.nips.cc/paper/6717-predictive-state-decoders-encoding-the-future-into-recurrent-networks
• Posterior sampling for reinforcement learning: worst-case regret bounds: https://papers.nips.cc/paper/6718-posterior-sampling-for-reinforcement-learning-worst-case-regret-bounds
• Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results: https://papers.nips.cc/paper/6719-mean-teachers-are-better-role-models-weight-averaged-consistency-targets-improve-semi-supervised-deep-learning-results
• Matching neural paths: transfer from recognition to correspondence search: https://papers.nips.cc/paper/6720-matching-neural-paths-transfer-from-recognition-to-correspondence-search
• Linearly constrained Gaussian processes: https://papers.nips.cc/paper/6721-linearly-constrained-gaussian-processes
• Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data: https://papers.nips.cc/paper/6722-fixed-rank-approximation-of-a-positive-semidefinite-matrix-from-streaming-data
• Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets: https://papers.nips.cc/paper/6723-multi-modal-imitation-learning-from-unstructured-demonstrations-using-generative-adversarial-nets
• Learning to Inpaint for Image Compression: https://papers.nips.cc/paper/6724-learning-to-inpaint-for-image-compression
• Adaptive Bayesian Sampling with Monte Carlo EM: https://papers.nips.cc/paper/6725-adaptive-bayesian-sampling-with-monte-carlo-em
• No More Fixed Penalty Parameter in ADMM: Faster Convergence with New Adaptive Penalization: https://papers.nips.cc/paper/6726-no-more-fixed-penalty-parameter-in-admm-faster-convergence-with-new-adaptive-penalization
• Shape and Material from Sound: https://papers.nips.cc/paper/6727-shape-and-material-from-sound
• Flexible statistical inference for mechanistic models of neural dynamics: https://papers.nips.cc/paper/6728-flexible-statistical-inference-for-mechanistic-models-of-neural-dynamics
• Online Prediction with Selfish Experts: https://papers.nips.cc/paper/6729-online-prediction-with-selfish-experts
• Tensor Biclustering: https://papers.nips.cc/paper/6730-tensor-biclustering
• DPSCREEN: Dynamic Personalized Screening: https://papers.nips.cc/paper/6731-dpscreen-dynamic-personalized-screening
• Learning Unknown Markov Decision Processes: A Thompson Sampling Approach: https://papers.nips.cc/paper/6732-learning-unknown-markov-decision-processes-a-thompson-sampling-approach
• Testing and Learning on Distributions with Symmetric Noise Invariance: https://papers.nips.cc/paper/6733-testing-and-learning-on-distributions-with-symmetric-noise-invariance
• A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering: https://papers.nips.cc/paper/6734-a-dirichlet-mixture-model-of-hawkes-processes-for-event-sequence-clustering
• Deanonymization in the Bitcoin P2P Network: https://papers.nips.cc/paper/6735-deanonymization-in-the-bitcoin-p2p-network
• Accelerated consensus via Min-Sum Splitting: https://papers.nips.cc/paper/6736-accelerated-consensus-via-min-sum-splitting
• Generalized Linear Model Regression under Distance-to-set Penalties: https://papers.nips.cc/paper/6737-generalized-linear-model-regression-under-distance-to-set-penalties
• Adaptive sampling for a population of neurons: https://papers.nips.cc/paper/6738-adaptive-sampling-for-a-population-of-neurons
• Nonbacktracking Bounds on the Influence in Independent Cascade Models: https://papers.nips.cc/paper/6739-nonbacktracking-bounds-on-the-influence-in-independent-cascade-models
• Learning with Feature Evolvable Streams: https://papers.nips.cc/paper/6740-learning-with-feature-evolvable-streams
• Online Convex Optimization with Stochastic Constraints: https://papers.nips.cc/paper/6741-online-convex-optimization-with-stochastic-constraints
• Max-Margin Invariant Features from Transformed Unlabelled Data: https://papers.nips.cc/paper/6742-max-margin-invariant-features-from-transformed-unlabelled-data
• Cognitive Impairment Prediction in Alzheimer’s Disease with Regularized Modal Regression: https://papers.nips.cc/paper/6743-cognitive-impairment-prediction-in-alzheimers-disease-with-regularized-modal-regression
• Translation Synchronization via Truncated Least Squares: https://papers.nips.cc/paper/6744-translation-synchronization-via-truncated-least-squares
• From which world is your graph: https://papers.nips.cc/paper/6745-from-which-world-is-your-graph
• A New Alternating Direction Method for Linear Programming: https://papers.nips.cc/paper/6746-a-new-alternating-direction-method-for-linear-programming
• Regret Analysis for Continuous Dueling Bandit: https://papers.nips.cc/paper/6747-regret-analysis-for-continuous-dueling-bandit
• Best Response Regression: https://papers.nips.cc/paper/6748-best-response-regression
• TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning: https://papers.nips.cc/paper/6749-terngrad-ternary-gradients-to-reduce-communication-in-distributed-deep-learning
• Learning Affinity via Spatial Propagation Networks: https://papers.nips.cc/paper/6750-learning-affinity-via-spatial-propagation-networks
• Linear regression without correspondence: https://papers.nips.cc/paper/6751-linear-regression-without-correspondence
• NeuralFDR: Learning Discovery Thresholds from Hypothesis Features: https://papers.nips.cc/paper/6752-neuralfdr-learning-discovery-thresholds-from-hypothesis-features
• Cost efficient gradient boosting: https://papers.nips.cc/paper/6753-cost-efficient-gradient-boosting
• Probabilistic Rule Realization and Selection: https://papers.nips.cc/paper/6754-probabilistic-rule-realization-and-selection
• Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions: https://papers.nips.cc/paper/6755-nearest-neighbor-sample-compression-efficiency-consistency-infinite-dimensions
• A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis: https://papers.nips.cc/paper/6756-a-scale-free-algorithm-for-stochastic-bandits-with-bounded-kurtosis
• Learning Multiple Tasks with Deep Relationship Networks: https://papers.nips.cc/paper/6757-learning-multiple-tasks-with-deep-relationship-networks
• Deep Hyperalignment: https://papers.nips.cc/paper/6758-deep-hyperalignment
• Online to Offline Conversions and Adaptive Minibatch Sizes: https://papers.nips.cc/paper/6759-online-to-offline-conversions-and-adaptive-minibatch-sizes
• Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure: https://papers.nips.cc/paper/6760-stochastic-optimization-with-variance-reduction-for-infinite-datasets-with-finite-sum-structure
• Deep Learning with Topological Signatures: https://papers.nips.cc/paper/6761-deep-learning-with-topological-signatures
• Predicting User Activity Level In Point Process Models With Mass Transport Equation: https://papers.nips.cc/paper/6762-predicting-user-activity-level-in-point-process-models-with-mass-transport-equation
• Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues: https://papers.nips.cc/paper/6763-submultiplicative-glivenko-cantelli-and-uniform-convergence-of-revenues
• Deep Dynamic Poisson Factorization Model: https://papers.nips.cc/paper/6764-deep-dynamic-poisson-factorization-model
• Positive-Unlabeled Learning with Non-Negative Risk Estimator: https://papers.nips.cc/paper/6765-positive-unlabeled-learning-with-non-negative-risk-estimator
• Optimal Sample Complexity of M-wise Data for Top-K Ranking: https://papers.nips.cc/paper/6766-optimal-sample-complexity-of-m-wise-data-for-top-k-ranking
• What-If Reasoning using Counterfactual Gaussian Processes: https://papers.nips.cc/paper/6767-what-if-reasoning-using-counterfactual-gaussian-processes
• Communication-Efficient Stochastic Gradient Descent, with Applications to Neural Networks: https://papers.nips.cc/paper/6768-communication-efficient-stochastic-gradient-descent-with-applications-to-neural-networks
• On the Convergence of Block Coordinate Descent in Training DNNs with Tikhonov Regularization: https://papers.nips.cc/paper/6769-on-the-convergence-of-block-coordinate-descent-in-training-dnns-with-tikhonov-regularization
• Train longer, generalize better: closing the generalization gap in large batch training of neural networks: https://papers.nips.cc/paper/6770-train-longer-generalize-better-closing-the-generalization-gap-in-large-batch-training-of-neural-networks
• Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks: https://papers.nips.cc/paper/6771-flexpoint-an-adaptive-numerical-format-for-efficient-training-of-deep-neural-networks
• Model evidence from nonequilibrium simulations: https://papers.nips.cc/paper/6772-model-evidence-from-nonequilibrium-simulations
• Minimal Exploration in Structured Stochastic Bandits: https://papers.nips.cc/paper/6773-minimal-exploration-in-structured-stochastic-bandits
• Learned D-AMP: Principled Neural-network-based Compressive Image Recovery: https://papers.nips.cc/paper/6774-learned-d-amp-principled-neural-network-based-compressive-image-recovery
• Deliberation Networks: Sequence Generation Beyond One-Pass Decoding: https://papers.nips.cc/paper/6775-deliberation-networks-sequence-generation-beyond-one-pass-decoding
• Adaptive Clustering through Semidefinite Programming: https://papers.nips.cc/paper/6776-adaptive-clustering-through-semidefinite-programming
• Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning: https://papers.nips.cc/paper/6777-log-normality-and-skewness-of-estimated-stateaction-values-in-reinforcement-learning
• Repeated Inverse Reinforcement Learning: https://papers.nips.cc/paper/6778-repeated-inverse-reinforcement-learning
• The Numerics of GANs: https://papers.nips.cc/paper/6779-the-numerics-of-gans
• Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search: https://papers.nips.cc/paper/6780-practical-bayesian-optimization-for-model-fitting-with-bayesian-adaptive-direct-search
• Learning Chordal Markov Networks via Branch and Bound: https://papers.nips.cc/paper/6781-learning-chordal-markov-networks-via-branch-and-bound
• Revenue Optimization with Approximate Bid Predictions: https://papers.nips.cc/paper/6782-revenue-optimization-with-approximate-bid-predictions
• Solving (Almost) all Systems of Random Quadratic Equations: https://papers.nips.cc/paper/6783-solving-almost-all-systems-of-random-quadratic-equations
• Unsupervised Learning of Disentangled Latent Representations from Sequential Data: https://papers.nips.cc/paper/6784-unsupervised-learning-of-disentangled-latent-representations-from-sequential-data
• Lookahead Bayesian Optimization with Inequality Constraints: https://papers.nips.cc/paper/6785-lookahead-bayesian-optimization-with-inequality-constraints
• Hierarchical Methods of Moments: https://papers.nips.cc/paper/6786-hierarchical-methods-of-moments
• Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts: https://papers.nips.cc/paper/6787-interpretable-and-globally-optimal-prediction-for-textual-grounding-using-image-concepts
• Revisit Fuzzy Neural Network: Demystifying Batch Normalization and ReLU with Generalized Hamming Network: https://papers.nips.cc/paper/6788-revisit-fuzzy-neural-network-demystifying-batch-normalization-and-relu-with-generalized-hamming-network
• Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization: https://papers.nips.cc/paper/6789-speeding-up-latent-variable-gaussian-graphical-model-estimation-via-nonconvex-optimization
• Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models: https://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models
• Generating steganographic images via adversarial training: https://papers.nips.cc/paper/6791-generating-steganographic-images-via-adversarial-training
• Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration: https://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration
• PixelGAN Autoencoders: https://papers.nips.cc/paper/6793-pixelgan-autoencoders
• Consistent Multitask Learning with Nonlinear Output Relations: https://papers.nips.cc/paper/6794-consistent-multitask-learning-with-nonlinear-output-relations
• Fast Alternating Minimization Algorithms for Dictionary Learning: https://papers.nips.cc/paper/6795-fast-alternating-minimization-algorithms-for-dictionary-learning
• Learning ReLUs via Gradient Descent: https://papers.nips.cc/paper/6796-learning-relus-via-gradient-descent
• Stabilizing Training of Generative Adversarial Networks through Regularization: https://papers.nips.cc/paper/6797-stabilizing-training-of-generative-adversarial-networks-through-regularization
• Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems: https://papers.nips.cc/paper/6798-expectation-propagation-with-stochastic-kinetic-model-in-complex-interaction-systems
• Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs: https://papers.nips.cc/paper/6799-data-efficient-reinforcement-learning-in-continuous-state-action-gaussian-pomdps
• Compatible Reward Inverse Reinforcement Learning: https://papers.nips.cc/paper/6800-compatible-reward-inverse-reinforcement-learning
• First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization: https://papers.nips.cc/paper/6801-first-order-adaptive-sample-size-methods-to-reduce-complexity-of-empirical-risk-minimization
• Hiding Images in Plain Sight: Deep Steganography: https://papers.nips.cc/paper/6802-hiding-images-in-plain-sight-deep-steganography
• Neural Program Meta-Induction: https://papers.nips.cc/paper/6803-neural-program-meta-induction
• Bayesian Dyadic Trees and Histograms for Regression: https://papers.nips.cc/paper/6804-bayesian-dyadic-trees-and-histograms-for-regression
• A graph-theoretic approach to multitasking: https://papers.nips.cc/paper/6805-a-graph-theoretic-approach-to-multitasking
• Consistent Robust Regression: https://papers.nips.cc/paper/6806-consistent-robust-regression
• Natural value approximators: learning when to trust past estimates: https://papers.nips.cc/paper/6807-natural-value-approximators-learning-when-to-trust-past-estimates
• Bandits Dueling on Partially Ordered Sets: https://papers.nips.cc/paper/6808-bandits-dueling-on-partially-ordered-sets
• Elementary Symmetric Polynomials for Optimal Experimental Design: https://papers.nips.cc/paper/6809-elementary-symmetric-polynomials-for-optimal-experimental-design
• Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols: https://papers.nips.cc/paper/6810-emergence-of-language-with-multi-agent-games-learning-to-communicate-with-sequences-of-symbols
• Backprop without Learning Rates Through Coin Betting: https://papers.nips.cc/paper/6811-backprop-without-learning-rates-through-coin-betting
• Pixels to Graphs by Associative Embedding: https://papers.nips.cc/paper/6812-pixels-to-graphs-by-associative-embedding
• Runtime Neural Pruning: https://papers.nips.cc/paper/6813-runtime-neural-pruning
• Compressing the Gram Matrix for Learning Neural Networks in Polynomial Time: https://papers.nips.cc/paper/6814-compressing-the-gram-matrix-for-learning-neural-networks-in-polynomial-time
• MMD GAN: Towards Deeper Understanding of Moment Matching Network: https://papers.nips.cc/paper/6815-mmd-gan-towards-deeper-understanding-of-moment-matching-network
• The Reversible Residual Network: Backpropagation Without Storing Activations: https://papers.nips.cc/paper/6816-the-reversible-residual-network-backpropagation-without-storing-activations
• Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe: https://papers.nips.cc/paper/6817-fast-rates-for-bandit-optimization-with-upper-confidence-frank-wolfe
• Zap Q-Learning: https://papers.nips.cc/paper/6818-zap-q-learning
• Expectation Propagation for t-Exponential Family Using Q-Algebra: https://papers.nips.cc/paper/6819-expectation-propagation-for-t-exponential-family-using-q-algebra
• Few-Shot Learning Through an Information Retrieval Lens: https://papers.nips.cc/paper/6820-few-shot-learning-through-an-information-retrieval-lens
• Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation: https://papers.nips.cc/paper/6821-formal-guarantees-on-the-robustness-of-a-classifier-against-adversarial-manipulation
• Associative Embedding: End-to-End Learning for Joint Detection and Grouping: https://papers.nips.cc/paper/6822-associative-embedding-end-to-end-learning-for-joint-detection-and-grouping
• Practical Locally Private Heavy Hitters: https://papers.nips.cc/paper/6823-practical-locally-private-heavy-hitters
• Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences: https://papers.nips.cc/paper/6824-large-scale-quadratically-constrained-quadratic-program-via-low-discrepancy-sequences
• Inhomogoenous Hypergraph Clustering with Applications: https://papers.nips.cc/paper/6825-inhomogoenous-hypergraph-clustering-with-applications
• Differentiable Learning of Logical Rules for Knowledge Base Reasoning: https://papers.nips.cc/paper/6826-differentiable-learning-of-logical-rules-for-knowledge-base-reasoning
• Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks: https://papers.nips.cc/paper/6827-deep-multi-task-gaussian-processes-for-survival-analysis-with-competing-risks
• Masked Autoregressive Flow for Density Estimation: https://papers.nips.cc/paper/6828-masked-autoregressive-flow-for-density-estimation
• Non-convex Finite-Sum Optimization Via SCSG Methods: https://papers.nips.cc/paper/6829-non-convex-finite-sum-optimization-via-scsg-methods
• Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting: https://papers.nips.cc/paper/6830-beyond-normality-learning-sparse-probabilistic-graphical-models-in-the-non-gaussian-setting
• Inner-loop free ADMM using Auxiliary Deep Neural Networks: https://papers.nips.cc/paper/6831-inner-loop-free-admm-using-auxiliary-deep-neural-networks
• OnACID: Online Analysis of Calcium Imaging Data in Real Time: https://papers.nips.cc/paper/6832-onacid-online-analysis-of-calcium-imaging-data-in-real-time
• Collaborative PAC Learning: https://papers.nips.cc/paper/6833-collaborative-pac-learning
• Fast Black-box Variational Inference through Stochastic Trust-Region Optimization: https://papers.nips.cc/paper/6834-fast-black-box-variational-inference-through-stochastic-trust-region-optimization
• Scalable Demand-Aware Recommendation: https://papers.nips.cc/paper/6835-scalable-demand-aware-recommendation
• SGD Learns the Conjugate Kernel Class of the Network: https://papers.nips.cc/paper/6836-sgd-learns-the-conjugate-kernel-class-of-the-network
• Noise-Tolerant Interactive Learning Using Pairwise Comparisons: https://papers.nips.cc/paper/6837-noise-tolerant-interactive-learning-using-pairwise-comparisons
• Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems: https://papers.nips.cc/paper/6838-analyzing-hidden-representations-in-end-to-end-automatic-speech-recognition-systems
• Generative Local Metric Learning for Kernel Regression: https://papers.nips.cc/paper/6839-generative-local-metric-learning-for-kernel-regression
• Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications: https://papers.nips.cc/paper/6840-information-theoretic-properties-of-markov-random-fields-and-their-algorithmic-applications
• Fitting Low-Rank Tensors in Constant Time: https://papers.nips.cc/paper/6841-fitting-low-rank-tensors-in-constant-time
• Deep supervised discrete hashing: https://papers.nips.cc/paper/6842-deep-supervised-discrete-hashing
• Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation: https://papers.nips.cc/paper/6843-using-options-and-covariance-testing-for-long-horizon-off-policy-policy-evaluation
• How regularization affects the critical points in linear networks: https://papers.nips.cc/paper/6844-how-regularization-affects-the-critical-points-in-linear-networks
• Fisher GAN: https://papers.nips.cc/paper/6845-fisher-gan
• Information-theoretic analysis of generalization capability of learning algorithms: https://papers.nips.cc/paper/6846-information-theoretic-analysis-of-generalization-capability-of-learning-algorithms
• Sparse Approximate Conic Hulls: https://papers.nips.cc/paper/6847-sparse-approximate-conic-hulls
• Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems: https://papers.nips.cc/paper/6848-rigorous-dynamics-and-consistent-estimation-in-arbitrarily-conditioned-linear-systems
• Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System: https://papers.nips.cc/paper/6849-toward-goal-driven-neural-network-models-for-the-rodent-whisker-trigeminal-system
• Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM: https://papers.nips.cc/paper/6850-accuracy-first-selecting-a-differential-privacy-level-for-accuracy-constrained-erm
• EX2: Exploration with Exemplar Models for Deep Reinforcement Learning: https://papers.nips.cc/paper/6851-ex2-exploration-with-exemplar-models-for-deep-reinforcement-learning
• Multitask Spectral Learning of Weighted Automata: https://papers.nips.cc/paper/6852-multitask-spectral-learning-of-weighted-automata
• Multi-way Interacting Regression via Factorization Machines: https://papers.nips.cc/paper/6853-multi-way-interacting-regression-via-factorization-machines
• Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network: https://papers.nips.cc/paper/6854-predicting-organic-reaction-outcomes-with-weisfeiler-lehman-network
• Practical Data-Dependent Metric Compression with Provable Guarantees: https://papers.nips.cc/paper/6855-practical-data-dependent-metric-compression-with-provable-guarantees
• REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models: https://papers.nips.cc/paper/6856-rebar-low-variance-unbiased-gradient-estimates-for-discrete-latent-variable-models
• Nonlinear random matrix theory for deep learning: https://papers.nips.cc/paper/6857-nonlinear-random-matrix-theory-for-deep-learning
• Parallel Streaming Wasserstein Barycenters: https://papers.nips.cc/paper/6858-parallel-streaming-wasserstein-barycenters
• ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games: https://papers.nips.cc/paper/6859-elf-an-extensive-lightweight-and-flexible-research-platform-for-real-time-strategy-games
• Dual Discriminator Generative Adversarial Nets: https://papers.nips.cc/paper/6860-dual-discriminator-generative-adversarial-nets
• Dynamic Revenue Sharing: https://papers.nips.cc/paper/6861-dynamic-revenue-sharing
• Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search: https://papers.nips.cc/paper/6862-decomposition-invariant-conditional-gradient-for-general-polytopes-with-line-search
• Multi-agent Predictive Modeling with Attentional CommNets: https://papers.nips.cc/paper/6863-multi-agent-predictive-modeling-with-attentional-commnets
• An Empirical Bayes Approach to Optimizing Machine Learning Algorithms: https://papers.nips.cc/paper/6864-an-empirical-bayes-approach-to-optimizing-machine-learning-algorithms
• Differentially Private Empirical Risk Minimization Revisited: Faster and More General: https://papers.nips.cc/paper/6865-differentially-private-empirical-risk-minimization-revisited-faster-and-more-general
• Variational Inference via \chi Upper Bound Minimization: https://papers.nips.cc/paper/6866-variational-inference-via-chi-upper-bound-minimization
• On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning: https://papers.nips.cc/paper/6867-on-quadratic-convergence-of-dc-proximal-newton-algorithm-in-nonconvex-sparse-learning
• #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning: https://papers.nips.cc/paper/6868-exploration-a-study-of-count-based-exploration-for-deep-reinforcement-learning
• An Empirical Study on The Properties of Random Bases for Kernel Methods: https://papers.nips.cc/paper/6869-an-empirical-study-on-the-properties-of-random-bases-for-kernel-methods
• Bridging the Gap Between Value and Policy Based Reinforcement Learning: https://papers.nips.cc/paper/6870-bridging-the-gap-between-value-and-policy-based-reinforcement-learning
• Premise Selection for Theorem Proving by Deep Graph Embedding: https://papers.nips.cc/paper/6871-premise-selection-for-theorem-proving-by-deep-graph-embedding
• A Bayesian Data Augmentation Approach for Learning Deep Models: https://papers.nips.cc/paper/6872-a-bayesian-data-augmentation-approach-for-learning-deep-models
• Principles of Riemannian Geometry in Neural Networks: https://papers.nips.cc/paper/6873-principles-of-riemannian-geometry-in-neural-networks
• Cold-Start Reinforcement Learning with Softmax Policy Gradients: https://papers.nips.cc/paper/6874-cold-start-reinforcement-learning-with-softmax-policy-gradients
• Online Dynamic Programming: https://papers.nips.cc/paper/6875-online-dynamic-programming
• Alternating Estimation for Structured High-Dimensional Multi-Response Models: https://papers.nips.cc/paper/6876-alternating-estimation-for-structured-high-dimensional-multi-response-models
• Convolutional Gaussian Processes: https://papers.nips.cc/paper/6877-convolutional-gaussian-processes
• Estimation of the covariance structure of heavy-tailed distributions: https://papers.nips.cc/paper/6878-estimation-of-the-covariance-structure-of-heavy-tailed-distributions
• Mean Field Residual Networks: On the Edge of Chaos: https://papers.nips.cc/paper/6879-mean-field-residual-networks-on-the-edge-of-chaos
• Decomposable Submodular Function Minimization: Discrete and Continuous: https://papers.nips.cc/paper/6880-decomposable-submodular-function-minimization-discrete-and-continuous
• Gauging Variational Inference: https://papers.nips.cc/paper/6881-gauging-variational-inference
• Deep Recurrent Neural Network-Based Identification of Precursor microRNAs: https://papers.nips.cc/paper/6882-deep-recurrent-neural-network-based-identification-of-precursor-micrornas
• Robust Estimation of Neural Signals in Calcium Imaging: https://papers.nips.cc/paper/6883-robust-estimation-of-neural-signals-in-calcium-imaging
• State Aware Imitation Learning: https://papers.nips.cc/paper/6884-state-aware-imitation-learning
• Beyond Parity: Fairness Objectives for Collaborative Filtering: https://papers.nips.cc/paper/6885-beyond-parity-fairness-objectives-for-collaborative-filtering
• A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent: https://papers.nips.cc/paper/6886-a-pac-bayesian-analysis-of-randomized-learning-with-application-to-stochastic-gradient-descent
• Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach: https://papers.nips.cc/paper/6887-fully-decentralized-policies-for-multi-agent-systems-an-information-theoretic-approach
• Model-Powered Conditional Independence Test: https://papers.nips.cc/paper/6888-model-powered-conditional-independence-test
• Deep Voice 2: Multi-Speaker Neural Text-to-Speech: https://papers.nips.cc/paper/6889-deep-voice-2-multi-speaker-neural-text-to-speech
• Variance-based Regularization with Convex Objectives: https://papers.nips.cc/paper/6890-variance-based-regularization-with-convex-objectives
• Deep Lattice Networks and Partial Monotonic Functions: https://papers.nips.cc/paper/6891-deep-lattice-networks-and-partial-monotonic-functions
• Continual Learning with Deep Generative Replay: https://papers.nips.cc/paper/6892-continual-learning-with-deep-generative-replay
• AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms: https://papers.nips.cc/paper/6893-aide-an-algorithm-for-measuring-the-accuracy-of-probabilistic-inference-algorithms
• Learning Causal Structures Using Regression Invariance: https://papers.nips.cc/paper/6894-learning-causal-structures-using-regression-invariance
• Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback: https://papers.nips.cc/paper/6895-online-influence-maximization-under-independent-cascade-model-with-semi-bandit-feedback
• Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem: https://papers.nips.cc/paper/6896-minimax-optimal-players-for-the-finite-time-3-expert-prediction-problem
• Reinforcement Learning under Model Mismatch: https://papers.nips.cc/paper/6897-reinforcement-learning-under-model-mismatch
• Hierarchical Attentive Recurrent Tracking: https://papers.nips.cc/paper/6898-hierarchical-attentive-recurrent-tracking
• Tomography of the London Underground: a Scalable Model for Origin-Destination Data: https://papers.nips.cc/paper/6899-tomography-of-the-london-underground-a-scalable-model-for-origin-destination-data
• Rotting Bandits: https://papers.nips.cc/paper/6900-rotting-bandits
• Unbiased estimates for linear regression via volume sampling: https://papers.nips.cc/paper/6901-unbiased-estimates-for-linear-regression-via-volume-sampling
• An Applied Algorithmic Foundation for Hierarchical Clustering: https://papers.nips.cc/paper/6902-an-applied-algorithmic-foundation-for-hierarchical-clustering
• Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition: https://papers.nips.cc/paper/6903-adaptive-accelerated-gradient-converging-method-under-holderian-error-bound-condition
• Stein Variational Gradient Descent as Gradient Flow: https://papers.nips.cc/paper/6904-stein-variational-gradient-descent-as-gradient-flow
• Partial Hard Thresholding: A Towards Unified Analysis of Support Recovery: https://papers.nips.cc/paper/6905-partial-hard-thresholding-a-towards-unified-analysis-of-support-recovery
• Shallow Updates for Deep Reinforcement Learning: https://papers.nips.cc/paper/6906-shallow-updates-for-deep-reinforcement-learning
• A Highly Efficient Gradient Boosting Decision Tree: https://papers.nips.cc/paper/6907-a-highly-efficient-gradient-boosting-decision-tree
• Adversarial Ranking for Language Generation: https://papers.nips.cc/paper/6908-adversarial-ranking-for-language-generation
• Regret Minimization in MDPs with Options without Prior Knowledge: https://papers.nips.cc/paper/6909-regret-minimization-in-mdps-with-options-without-prior-knowledge
• Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee: https://papers.nips.cc/paper/6910-net-trim-convex-pruning-of-deep-neural-networks-with-performance-guarantee
• Graph Matching via Multiplicative Update Algorithm: https://papers.nips.cc/paper/6911-graph-matching-via-multiplicative-update-algorithm
• Dynamic Importance Sampling for Anytime Bounds of the Partition Function: https://papers.nips.cc/paper/6912-dynamic-importance-sampling-for-anytime-bounds-of-the-partition-function
• Is the Bellman residual a bad proxy?: https://papers.nips.cc/paper/6913-is-the-bellman-residual-a-bad-proxy
• Generalization Properties of Learning with Random Features: https://papers.nips.cc/paper/6914-generalization-properties-of-learning-with-random-features
• Differentially private Bayesian learning on distributed data: https://papers.nips.cc/paper/6915-differentially-private-bayesian-learning-on-distributed-data
• Learning to Compose Domain-Specific Transformations for Data Augmentation: https://papers.nips.cc/paper/6916-learning-to-compose-domain-specific-transformations-for-data-augmentation
• Wasserstein Learning of Deep Generative Point Process Models: https://papers.nips.cc/paper/6917-wasserstein-learning-of-deep-generative-point-process-models
• Ensemble Sampling: https://papers.nips.cc/paper/6918-ensemble-sampling
• Language modeling with recurrent highway hypernetworks: https://papers.nips.cc/paper/6919-language-modeling-with-recurrent-highway-hypernetworks
• Searching in the Dark: Practical SVRG Methods under Error Bound Conditions with Guarantee: https://papers.nips.cc/paper/6920-searching-in-the-dark-practical-svrg-methods-under-error-bound-conditions-with-guarantee
• Bayesian Compression for Deep Learning: https://papers.nips.cc/paper/6921-bayesian-compression-for-deep-learning
• Streaming Sparse Gaussian Process Approximations: https://papers.nips.cc/paper/6922-streaming-sparse-gaussian-process-approximations
• VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning: https://papers.nips.cc/paper/6923-veegan-reducing-mode-collapse-in-gans-using-implicit-variational-learning
• Sparse k-Means Embedding: https://papers.nips.cc/paper/6924-sparse-k-means-embedding
• Utile Context Tree Weighting: https://papers.nips.cc/paper/6925-utile-context-tree-weighting
• A Regularized Framework for Sparse and Structured Neural Attention: https://papers.nips.cc/paper/6926-a-regularized-framework-for-sparse-and-structured-neural-attention
• Multi-output Polynomial Networks and Factorization Machines: https://papers.nips.cc/paper/6927-multi-output-polynomial-networks-and-factorization-machines
• Clustering Billions of Reads for DNA Data Storage: https://papers.nips.cc/paper/6928-clustering-billions-of-reads-for-dna-data-storage
• Multi-Objective Non-parametric Sequential Prediction: https://papers.nips.cc/paper/6929-multi-objective-non-parametric-sequential-prediction
• A Universal Analysis of Large-Scale Regularized Least Squares Solutions: https://papers.nips.cc/paper/6930-a-universal-analysis-of-large-scale-regularized-least-squares-solutions
• Deep Sets: https://papers.nips.cc/paper/6931-deep-sets
• ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events: https://papers.nips.cc/paper/6932-extremeweather-a-large-scale-climate-dataset-for-semi-supervised-detection-localization-and-understanding-of-extreme-weather-events
• Process-constrained batch Bayesian optimisation: https://papers.nips.cc/paper/6933-process-constrained-batch-bayesian-optimisation
• Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes: https://papers.nips.cc/paper/6934-bayesian-inference-of-individualized-treatment-effects-using-multi-task-gaussian-processes
• Spherical convolutions and their application in molecular modelling: https://papers.nips.cc/paper/6935-spherical-convolutions-and-their-application-in-molecular-modelling
• Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding: https://papers.nips.cc/paper/6936-efficient-optimization-for-linear-dynamical-systems-with-applications-to-clustering-and-sparse-coding
• On Optimal Generalizability in Parametric Learning: https://papers.nips.cc/paper/6937-on-optimal-generalizability-in-parametric-learning
• Near Optimal Sketching of Low-Rank Tensor Regression: https://papers.nips.cc/paper/6938-near-optimal-sketching-of-low-rank-tensor-regression
• Tractability in Structured Probability Spaces: https://papers.nips.cc/paper/6939-tractability-in-structured-probability-spaces
• Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit: https://papers.nips.cc/paper/6940-model-based-bayesian-inference-of-neural-activity-and-connectivity-from-all-optical-interrogation-of-a-neural-circuit
• Gaussian process based nonlinear latent structure discovery in multivariate spike train data: https://papers.nips.cc/paper/6941-gaussian-process-based-nonlinear-latent-structure-discovery-in-multivariate-spike-train-data
• Neural system identification for large populations separating "what" and "where": https://papers.nips.cc/paper/6942-neural-system-identification-for-large-populations-separating-what-and-where
• Certified Defenses for Data Poisoning Attacks: https://papers.nips.cc/paper/6943-certified-defenses-for-data-poisoning-attacks
• Eigen-Distortions of Hierarchical Representations: https://papers.nips.cc/paper/6944-eigen-distortions-of-hierarchical-representations
• Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization: https://papers.nips.cc/paper/6945-limitations-on-variance-reduction-and-acceleration-schemes-for-finite-sums-optimization
• Unsupervised Sequence Classification using Sequential Output Statistics: https://papers.nips.cc/paper/6946-unsupervised-sequence-classification-using-sequential-output-statistics
• Subset Selection under Noise: https://papers.nips.cc/paper/6947-subset-selection-under-noise
• Collecting Telemetry Data Privately: https://papers.nips.cc/paper/6948-collecting-telemetry-data-privately
• Concrete Dropout: https://papers.nips.cc/paper/6949-concrete-dropout
• Adaptive Batch Size for Safe Policy Gradients: https://papers.nips.cc/paper/6950-adaptive-batch-size-for-safe-policy-gradients
• A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning: https://papers.nips.cc/paper/6951-a-disentangled-recognition-and-nonlinear-dynamics-model-for-unsupervised-learning
• PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference: https://papers.nips.cc/paper/6952-pass-glm-polynomial-approximate-sufficient-statistics-for-scalable-bayesian-glm-inference
• Bayesian GANs: https://papers.nips.cc/paper/6953-bayesian-gans
• Off-policy evaluation for slate recommendation: https://papers.nips.cc/paper/6954-off-policy-evaluation-for-slate-recommendation
• A multi-agent reinforcement learning model of common-pool resource appropriation: https://papers.nips.cc/paper/6955-a-multi-agent-reinforcement-learning-model-of-common-pool-resource-appropriation
• On the Optimization Landscape of Tensor Decompositions: https://papers.nips.cc/paper/6956-on-the-optimization-landscape-of-tensor-decompositions
• High-Order Attention Models for Visual Question Answering: https://papers.nips.cc/paper/6957-high-order-attention-models-for-visual-question-answering
• Sparse convolutional coding for neuronal assembly detection: https://papers.nips.cc/paper/6958-sparse-convolutional-coding-for-neuronal-assembly-detection
• Quantifying how much sensory information in a neural code is relevant for behavior: https://papers.nips.cc/paper/6959-quantifying-how-much-sensory-information-in-a-neural-code-is-relevant-for-behavior
• Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks: https://papers.nips.cc/paper/6960-geometric-matrix-completion-with-recurrent-multi-graph-neural-networks
• Reducing Reparameterization Gradient Variance: https://papers.nips.cc/paper/6961-reducing-reparameterization-gradient-variance
• Visual Reference Resolution using Attention Memory for Visual Dialog: https://papers.nips.cc/paper/6962-visual-reference-resolution-using-attention-memory-for-visual-dialog
• Joint distribution optimal transportation for domain adaptation: https://papers.nips.cc/paper/6963-joint-distribution-optimal-transportation-for-domain-adaptation
• Multiresolution Kernel Approximation for Gaussian Process Regression: https://papers.nips.cc/paper/6964-multiresolution-kernel-approximation-for-gaussian-process-regression
• Collapsed variational Bayes for Markov jump processes: https://papers.nips.cc/paper/6965-collapsed-variational-bayes-for-markov-jump-processes
• Universal consistency and minimax rates for online Mondrian Forest: https://papers.nips.cc/paper/6966-universal-consistency-and-minimax-rates-for-online-mondrian-forest
• Efficiency Guarantees from Data: https://papers.nips.cc/paper/6967-efficiency-guarantees-from-data
• Diving into the shallows: a computational perspective on large-scale shallow learning: https://papers.nips.cc/paper/6968-diving-into-the-shallows-a-computational-perspective-on-large-scale-shallow-learning
• End-to-end Differentiable Proving: https://papers.nips.cc/paper/6969-end-to-end-differentiable-proving
• Influence Maximization with \varepsilon-Almost Submodular Threshold Function: https://papers.nips.cc/paper/6970-influence-maximization-with-varepsilon-almost-submodular-threshold-function
• Inferring The Latent Structure of Human Decision-Making from Raw Visual Inputs: https://papers.nips.cc/paper/6971-inferring-the-latent-structure-of-human-decision-making-from-raw-visual-inputs
• Variational Laws of Visual Attention for Dynamic Scenes: https://papers.nips.cc/paper/6972-variational-laws-of-visual-attention-for-dynamic-scenes
• Recursive Sampling for the Nystrom Method: https://papers.nips.cc/paper/6973-recursive-sampling-for-the-nystrom-method
• Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning: https://papers.nips.cc/paper/6974-interpolated-policy-gradient-merging-on-policy-and-off-policy-gradient-estimation-for-deep-reinforcement-learning
• Dynamic Routing Between Capsules: https://papers.nips.cc/paper/6975-dynamic-routing-between-capsules
• Incorporating Side Information by Adaptive Convolution: https://papers.nips.cc/paper/6976-incorporating-side-information-by-adaptive-convolution
• Conic Scan Coverage algorithm for nonparametric topic modeling: https://papers.nips.cc/paper/6977-conic-scan-coverage-algorithm-for-nonparametric-topic-modeling
• FALKON: An Optimal Large Scale Kernel Method: https://papers.nips.cc/paper/6978-falkon-an-optimal-large-scale-kernel-method
• Structured Generative Adversarial Networks: https://papers.nips.cc/paper/6979-structured-generative-adversarial-networks
• Conservative Contextual Linear Bandits: https://papers.nips.cc/paper/6980-conservative-contextual-linear-bandits
• Variational Memory Addressing in Generative Models: https://papers.nips.cc/paper/6981-variational-memory-addressing-in-generative-models
• On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm: https://papers.nips.cc/paper/6982-on-tensor-train-rank-minimization-statistical-efficiency-and-scalable-algorithm
• Scalable Levy Process Priors for Spectral Kernel Learning: https://papers.nips.cc/paper/6983-scalable-levy-process-priors-for-spectral-kernel-learning
• Deep Hyperspherical Learning: https://papers.nips.cc/paper/6984-deep-hyperspherical-learning
• Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction: https://papers.nips.cc/paper/6985-learning-deep-structured-multi-scale-features-using-attention-gated-crfs-for-contour-prediction
• On-the-fly Operation Batching in Dynamic Computation Graphs: https://papers.nips.cc/paper/6986-on-the-fly-operation-batching-in-dynamic-computation-graphs
• Nonlinear Acceleration of Stochastic Algorithms: https://papers.nips.cc/paper/6987-nonlinear-acceleration-of-stochastic-algorithms
• Optimized Pre-Processing for Discrimination Prevention: https://papers.nips.cc/paper/6988-optimized-pre-processing-for-discrimination-prevention
• YASS: Yet Another Spike Sorter: https://papers.nips.cc/paper/6989-yass-yet-another-spike-sorter
• Independence clustering (without a matrix): https://papers.nips.cc/paper/6990-independence-clustering-without-a-matrix
• Fast amortized inference of neural activity from calcium imaging data with variational autoencoders: https://papers.nips.cc/paper/6991-fast-amortized-inference-of-neural-activity-from-calcium-imaging-data-with-variational-autoencoders
• Adaptive Active Hypothesis Testing under Limited Information: https://papers.nips.cc/paper/6992-adaptive-active-hypothesis-testing-under-limited-information
• Streaming Weak Submodularity: Interpreting Neural Networks on the Fly: https://papers.nips.cc/paper/6993-streaming-weak-submodularity-interpreting-neural-networks-on-the-fly
• Successor Features for Transfer in Reinforcement Learning: https://papers.nips.cc/paper/6994-successor-features-for-transfer-in-reinforcement-learning
• Counterfactual Fairness: https://papers.nips.cc/paper/6995-counterfactual-fairness
• Prototypical Networks for Few-shot Learning: https://papers.nips.cc/paper/6996-prototypical-networks-for-few-shot-learning
• Triple Generative Adversarial Nets: https://papers.nips.cc/paper/6997-triple-generative-adversarial-nets
• Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression: https://papers.nips.cc/paper/6998-efficient-sublinear-regret-algorithms-for-online-sparse-linear-regression
• Mapping distinct timescales of functional interactions among brain networks: https://papers.nips.cc/paper/6999-mapping-distinct-timescales-of-functional-interactions-among-brain-networks
• Multi-Armed Bandits with Metric Movement Costs: https://papers.nips.cc/paper/7000-multi-armed-bandits-with-metric-movement-costs
• Learning A Structured Optimal Bipartite Graph for Co-Clustering: https://papers.nips.cc/paper/7001-learning-a-structured-optimal-bipartite-graph-for-co-clustering
• Learning Low-Dimensional Metrics: https://papers.nips.cc/paper/7002-learning-low-dimensional-metrics
• The Marginal Value of Adaptive Gradient Methods in Machine Learning: https://papers.nips.cc/paper/7003-the-marginal-value-of-adaptive-gradient-methods-in-machine-learning
• Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification: https://papers.nips.cc/paper/7004-aggressive-sampling-for-multi-class-to-binary-reduction-with-applications-to-text-classification
• Deconvolutional Paragraph Representation Learning: https://papers.nips.cc/paper/7005-deconvolutional-paragraph-representation-learning
• Random Permutation Online Isotonic Regression: https://papers.nips.cc/paper/7006-random-permutation-online-isotonic-regression
• A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning: https://papers.nips.cc/paper/7007-a-unified-game-theoretic-approach-to-multiagent-reinforcement-learning
• Inverse Filtering for Hidden Markov Models: https://papers.nips.cc/paper/7008-inverse-filtering-for-hidden-markov-models
• Non-parametric Neural Networks: https://papers.nips.cc/paper/7009-non-parametric-neural-networks
• Learning Active Learning from Data: https://papers.nips.cc/paper/7010-learning-active-learning-from-data
• VAE Learning via Stein Variational Gradient Descent: https://papers.nips.cc/paper/7011-vae-learning-via-stein-variational-gradient-descent
• Deep adversarial neural decoding: https://papers.nips.cc/paper/7012-deep-adversarial-neural-decoding
• Efficient Use of Limited-Memory Resources to Accelerate Linear Learning: https://papers.nips.cc/paper/7013-efficient-use-of-limited-memory-resources-to-accelerate-linear-learning
• Temporal Coherency based Criteria for Predicting Video Frames using Deep Multi-stage Generative Adversarial Networks: https://papers.nips.cc/paper/7014-temporal-coherency-based-criteria-for-predicting-video-frames-using-deep-multi-stage-generative-adversarial-networks
• Sobolev Training for Neural Networks: https://papers.nips.cc/paper/7015-sobolev-training-for-neural-networks
• Multi-Information Source Optimization: https://papers.nips.cc/paper/7016-multi-information-source-optimization
• Deep Reinforcement Learning from Human Preferences: https://papers.nips.cc/paper/7017-deep-reinforcement-learning-from-human-preferences
• On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks: https://papers.nips.cc/paper/7018-on-the-fine-grained-complexity-of-empirical-risk-minimization-kernel-methods-and-neural-networks
• Policy Gradient With Value Function Approximation For Collective Multiagent Planning: https://papers.nips.cc/paper/7019-policy-gradient-with-value-function-approximation-for-collective-multiagent-planning
• Adversarial Symmetric Variational Autoencoder: https://papers.nips.cc/paper/7020-adversarial-symmetric-variational-autoencoder
• Tensor encoding and decomposition of brain connectomes with application to tractography evaluation: https://papers.nips.cc/paper/7021-tensor-encoding-and-decomposition-of-brain-connectomes-with-application-to-tractography-evaluation
• A Minimax Optimal Algorithm for Crowdsourcing: https://papers.nips.cc/paper/7022-a-minimax-optimal-algorithm-for-crowdsourcing
• Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach: https://papers.nips.cc/paper/7023-estimating-accuracy-from-unlabeled-data-a-probabilistic-logic-approach
• A Decomposition of Forecast Error in Prediction Markets: https://papers.nips.cc/paper/7024-a-decomposition-of-forecast-error-in-prediction-markets
• Safe Adaptive Importance Sampling: https://papers.nips.cc/paper/7025-safe-adaptive-importance-sampling
• Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net: https://papers.nips.cc/paper/7026-variational-walkback-learning-a-transition-operator-as-a-stochastic-recurrent-net
• Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication: https://papers.nips.cc/paper/7027-polynomial-codes-an-optimal-design-for-high-dimensional-coded-matrix-multiplication
• Unsupervised Learning of Disentangled Representations from Video: https://papers.nips.cc/paper/7028-unsupervised-learning-of-disentangled-representations-from-video
• Federated Multi-Task Learning: https://papers.nips.cc/paper/7029-federated-multi-task-learning
• Is Input Sparsity Time Possible for Kernel Low-Rank Approximation?: https://papers.nips.cc/paper/7030-is-input-sparsity-time-possible-for-kernel-low-rank-approximation
• The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities: https://papers.nips.cc/paper/7031-the-expxorcist-nonparametric-graphical-models-via-conditional-exponential-densities
• Improved Graph Laplacian via Geometric Self-Consistency: https://papers.nips.cc/paper/7032-improved-graph-laplacian-via-geometric-self-consistency
• Dual Path Networks: https://papers.nips.cc/paper/7033-dual-path-networks
• Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers: https://papers.nips.cc/paper/7034-faster-and-non-ergodic-o1k-stochastic-alternating-direction-method-of-multipliers
• A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks: https://papers.nips.cc/paper/7035-a-probabilistic-framework-for-nonlinearities-in-stochastic-neural-networks
• DisTraL: Robust multitask reinforcement learning: https://papers.nips.cc/paper/7036-distral-robust-multitask-reinforcement-learning
• Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions: https://papers.nips.cc/paper/7037-online-learning-of-optimal-bidding-strategy-in-repeated-multi-commodity-auctions
• Trimmed Density Ratio Estimation: https://papers.nips.cc/paper/7038-trimmed-density-ratio-estimation
• Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems: https://papers.nips.cc/paper/7039-training-recurrent-networks-to-generate-hypotheses-about-how-the-brain-solves-hard-navigation-problems
• Visual Interaction Networks: https://papers.nips.cc/paper/7040-visual-interaction-networks
• Reconstruct & Crush Network: https://papers.nips.cc/paper/7041-reconstruct-crush-network
• Streaming Robust Submodular Maximization:A Partitioned Thresholding Approach: https://papers.nips.cc/paper/7042-streaming-robust-submodular-maximizationa-partitioned-thresholding-approach
• Simple strategies for recovering inner products from coarsely quantized random projections: https://papers.nips.cc/paper/7043-simple-strategies-for-recovering-inner-products-from-coarsely-quantized-random-projections
• Discovering Potential Influence via Information Bottleneck: https://papers.nips.cc/paper/7044-discovering-potential-influence-via-information-bottleneck
• Doubly Stochastic Variational Inference for Deep Gaussian Processes: https://papers.nips.cc/paper/7045-doubly-stochastic-variational-inference-for-deep-gaussian-processes
• Ranking Data with Continuous Labels through Oriented Recursive Partitions: https://papers.nips.cc/paper/7046-ranking-data-with-continuous-labels-through-oriented-recursive-partitions
• Scalable Model Selection for Belief Networks: https://papers.nips.cc/paper/7047-scalable-model-selection-for-belief-networks
• Targeting EEG/LFP Synchrony with Neural Nets: https://papers.nips.cc/paper/7048-targeting-eeglfp-synchrony-with-neural-nets
• Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs: https://papers.nips.cc/paper/7049-near-optimal-edge-evaluation-in-explicit-generalized-binomial-graphs
• Non-Stationary Spectral Kernels: https://papers.nips.cc/paper/7050-non-stationary-spectral-kernels
• Overcoming Catastrophic Forgetting by Incremental Moment Matching: https://papers.nips.cc/paper/7051-overcoming-catastrophic-forgetting-by-incremental-moment-matching
• Balancing information exposure in social networks: https://papers.nips.cc/paper/7052-balancing-information-exposure-in-social-networks
• SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud: https://papers.nips.cc/paper/7053-safetynets-verifiable-execution-of-deep-neural-networks-on-an-untrusted-cloud
• Query Complexity of Clustering with Side Information: https://papers.nips.cc/paper/7054-query-complexity-of-clustering-with-side-information
• QMDP-Net: Deep Learning for Planning under Partial Observability: https://papers.nips.cc/paper/7055-qmdp-net-deep-learning-for-planning-under-partial-observability
• Robust Optimization for Non-Convex Objectives: https://papers.nips.cc/paper/7056-robust-optimization-for-non-convex-objectives
• Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation: https://papers.nips.cc/paper/7057-thy-friend-is-my-friend-iterative-collaborative-filtering-for-sparse-matrix-estimation
• Adaptive Classification for Prediction Under a Budget: https://papers.nips.cc/paper/7058-adaptive-classification-for-prediction-under-a-budget
• Convergence rates of a partition based Bayesian multivariate density estimation method: https://papers.nips.cc/paper/7059-convergence-rates-of-a-partition-based-bayesian-multivariate-density-estimation-method
• Affine-Invariant Online Optimization: https://papers.nips.cc/paper/7060-affine-invariant-online-optimization
• Beyond Worst-case: A Probabilistic Analysis of Affine Policies in Dynamic Optimization: https://papers.nips.cc/paper/7061-beyond-worst-case-a-probabilistic-analysis-of-affine-policies-in-dynamic-optimization
• A unified approach to interpreting model predictions: https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions
• Stochastic Approximation for Canonical Correlation Analysis: https://papers.nips.cc/paper/7063-stochastic-approximation-for-canonical-correlation-analysis
• Investigating the learning dynamics of deep neural networks using random matrix theory: https://papers.nips.cc/paper/7064-investigating-the-learning-dynamics-of-deep-neural-networks-using-random-matrix-theory
• Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions: https://papers.nips.cc/paper/7065-sample-and-computationally-efficient-learning-algorithms-under-s-concave-distributions
• Scalable Variational Inference for Dynamical Systems: https://papers.nips.cc/paper/7066-scalable-variational-inference-for-dynamical-systems
• Context Selection for Embedding Models: https://papers.nips.cc/paper/7067-context-selection-for-embedding-models
• Working hard to know your neighbor's margins: Local descriptor learning loss: https://papers.nips.cc/paper/7068-working-hard-to-know-your-neighbors-margins-local-descriptor-learning-loss
• Accelerated Stochastic Greedy Coordinate Descent by Soft Thresholding Projection onto Simplex: https://papers.nips.cc/paper/7069-accelerated-stochastic-greedy-coordinate-descent-by-soft-thresholding-projection-onto-simplex
• Multi-Task Learning for Contextual Bandits: https://papers.nips.cc/paper/7070-multi-task-learning-for-contextual-bandits
• Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon: https://papers.nips.cc/paper/7071-learning-to-prune-deep-neural-networks-via-layer-wise-optimal-brain-surgeon
• Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds: https://papers.nips.cc/paper/7072-accelerated-first-order-methods-for-geodesically-convex-optimization-on-riemannian-manifolds
• Selective Classification for Deep Neural Networks: https://papers.nips.cc/paper/7073-selective-classification-for-deep-neural-networks
• Minimax Estimation of Bandable Precision Matrices: https://papers.nips.cc/paper/7074-minimax-estimation-of-bandable-precision-matrices
• Monte-Carlo Tree Search by Best Arm Identification: https://papers.nips.cc/paper/7075-monte-carlo-tree-search-by-best-arm-identification
• Group Additive Structure Identification for Kernel Nonparametric Regression: https://papers.nips.cc/paper/7076-group-additive-structure-identification-for-kernel-nonparametric-regression
• Fast, Sample-Efficient Algorithms for Structured Phase Retrieval: https://papers.nips.cc/paper/7077-fast-sample-efficient-algorithms-for-structured-phase-retrieval
• Hash Embeddings for Efficient Word Representations: https://papers.nips.cc/paper/7078-hash-embeddings-for-efficient-word-representations
• Online Learning for Multivariate Hawkes Processes: https://papers.nips.cc/paper/7079-online-learning-for-multivariate-hawkes-processes
• Maximum Margin Interval Trees: https://papers.nips.cc/paper/7080-maximum-margin-interval-trees
• DropoutNet: Addressing Cold Start in Recommender Systems: https://papers.nips.cc/paper/7081-dropoutnet-addressing-cold-start-in-recommender-systems
• A simple neural network module for relational reasoning: https://papers.nips.cc/paper/7082-a-simple-neural-network-module-for-relational-reasoning
• Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes: https://papers.nips.cc/paper/7083-q-lda-uncovering-latent-patterns-in-text-based-sequential-decision-processes
• Online Reinforcement Learning in Stochastic Games: https://papers.nips.cc/paper/7084-online-reinforcement-learning-in-stochastic-games
• Position-based Multiple-play Multi-armed Bandit Problem with Unknown Position Bias: https://papers.nips.cc/paper/7085-position-based-multiple-play-multi-armed-bandit-problem-with-unknown-position-bias
• Active Exploration for Learning Symbolic Representations: https://papers.nips.cc/paper/7086-active-exploration-for-learning-symbolic-representations
• Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling: https://papers.nips.cc/paper/7087-clone-mcmc-parallel-high-dimensional-gaussian-gibbs-sampling
• Fair Clustering Through Fairlets: https://papers.nips.cc/paper/7088-fair-clustering-through-fairlets
• Polynomial time algorithms for dual volume sampling: https://papers.nips.cc/paper/7089-polynomial-time-algorithms-for-dual-volume-sampling
• Hindsight Experience Replay: https://papers.nips.cc/paper/7090-hindsight-experience-replay
• Stochastic and Adversarial Online Learning without Hyperparameters: https://papers.nips.cc/paper/7091-stochastic-and-adversarial-online-learning-without-hyperparameters
• Teaching Machines to Describe Images with Natural Language Feedback: https://papers.nips.cc/paper/7092-teaching-machines-to-describe-images-with-natural-language-feedback
• Perturbative Black Box Variational Inference: https://papers.nips.cc/paper/7093-perturbative-black-box-variational-inference
• GibbsNet: Iterative Adversarial Inference for Deep Graphical Models: https://papers.nips.cc/paper/7094-gibbsnet-iterative-adversarial-inference-for-deep-graphical-models
• PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space: https://papers.nips.cc/paper/7095-pointnet-deep-hierarchical-feature-learning-on-point-sets-in-a-metric-space
• Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization: https://papers.nips.cc/paper/7096-regularizing-deep-neural-networks-by-noise-its-interpretation-and-optimization
• Learning Graph Embeddings with Embedding Propagation: https://papers.nips.cc/paper/7097-learning-graph-embeddings-with-embedding-propagation
• Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes: https://papers.nips.cc/paper/7098-efficient-modeling-of-latent-information-in-supervised-learning-using-gaussian-processes
• A-NICE-MC: Adversarial Training for MCMC: https://papers.nips.cc/paper/7099-a-nice-mc-adversarial-training-for-mcmc
• Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models: https://papers.nips.cc/paper/7100-excess-risk-bounds-for-the-bayes-risk-using-variational-inference-in-latent-gaussian-models
• Real-Time Bidding with Side Information: https://papers.nips.cc/paper/7101-real-time-bidding-with-side-information
• Saliency-based Sequential Image Attention with Multiset Prediction: https://papers.nips.cc/paper/7102-saliency-based-sequential-image-attention-with-multiset-prediction
• Variational Inference for Gaussian Process Models with Linear Complexity: https://papers.nips.cc/paper/7103-variational-inference-for-gaussian-process-models-with-linear-complexity
• K-Medoids For K-Means Seeding: https://papers.nips.cc/paper/7104-k-medoids-for-k-means-seeding
• Identifying Outlier Arms in Multi-Armed Bandit: https://papers.nips.cc/paper/7105-identifying-outlier-arms-in-multi-armed-bandit
• Online Learning with Transductive Regret: https://papers.nips.cc/paper/7106-online-learning-with-transductive-regret
• Riemannian approach to batch normalization: https://papers.nips.cc/paper/7107-riemannian-approach-to-batch-normalization
• Self-supervised Learning of Motion Capture: https://papers.nips.cc/paper/7108-self-supervised-learning-of-motion-capture
• Triangle Generative Adversarial Networks: https://papers.nips.cc/paper/7109-triangle-generative-adversarial-networks
• Preserving Proximity and Global Ranking for Node Embedding: https://papers.nips.cc/paper/7110-preserving-proximity-and-global-ranking-for-node-embedding
• Bayesian Optimization with Gradients: https://papers.nips.cc/paper/7111-bayesian-optimization-with-gradients
• Second-order Optimization in Deep Reinforcement Learning using Kronecker-factored Approximation: https://papers.nips.cc/paper/7112-second-order-optimization-in-deep-reinforcement-learning-using-kronecker-factored-approximation
• Renyi Differential Privacy Mechanisms for Posterior Sampling: https://papers.nips.cc/paper/7113-renyi-differential-privacy-mechanisms-for-posterior-sampling
• Online Learning with a Hint: https://papers.nips.cc/paper/7114-online-learning-with-a-hint
• Identification of Gaussian Process State Space Models: https://papers.nips.cc/paper/7115-identification-of-gaussian-process-state-space-models
• Robust Imitation of Diverse Behaviors: https://papers.nips.cc/paper/7116-robust-imitation-of-diverse-behaviors
• Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent: https://papers.nips.cc/paper/7117-can-decentralized-algorithms-outperform-centralized-algorithms-a-case-study-for-decentralized-parallel-stochastic-gradient-descent
• Local Aggregative Games: https://papers.nips.cc/paper/7118-local-aggregative-games
• A Sample Complexity Measure with Applications to Learning Optimal Auctions: https://papers.nips.cc/paper/7119-a-sample-complexity-measure-with-applications-to-learning-optimal-auctions
• Thinking Fast and Slow with Deep Learning and Tree Search: https://papers.nips.cc/paper/7120-thinking-fast-and-slow-with-deep-learning-and-tree-search
• EEG-GRAPH: A Factor Graph Based Model for Capturing Spatial, Temporal, and Observational Relationships in Electroencephalograms: https://papers.nips.cc/paper/7121-eeg-graph-a-factor-graph-based-model-for-capturing-spatial-temporal-and-observational-relationships-in-electroencephalograms
• Improving the Expected Improvement Algorithm: https://papers.nips.cc/paper/7122-improving-the-expected-improvement-algorithm
• Hybrid Reward Architecture for Reinforcement Learning: https://papers.nips.cc/paper/7123-hybrid-reward-architecture-for-reinforcement-learning
• Approximate Supermodularity Bounds for Experimental Design: https://papers.nips.cc/paper/7124-approximate-supermodularity-bounds-for-experimental-design
• Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification: https://papers.nips.cc/paper/7125-maximizing-subset-accuracy-with-recurrent-neural-networks-in-multi-label-classification
• AdaGAN: Boosting Generative Models: https://papers.nips.cc/paper/7126-adagan-boosting-generative-models
• Straggler Mitigation in Distributed Optimization Through Data Encoding: https://papers.nips.cc/paper/7127-straggler-mitigation-in-distributed-optimization-through-data-encoding
• Multi-View Decision Processes: https://papers.nips.cc/paper/7128-multi-view-decision-processes
• A Greedy Approach for Budgeted Maximum Inner Product Search: https://papers.nips.cc/paper/7129-a-greedy-approach-for-budgeted-maximum-inner-product-search
• SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks: https://papers.nips.cc/paper/7130-svd-softmax-fast-softmax-approximation-on-large-vocabulary-neural-networks
• Plan, Attend, Generate: Planning for Sequence-to-Sequence Models: https://papers.nips.cc/paper/7131-plan-attend-generate-planning-for-sequence-to-sequence-models
• Task-based End-to-end Model Learning in Stochastic Optimization: https://papers.nips.cc/paper/7132-task-based-end-to-end-model-learning-in-stochastic-optimization
• Towards Understanding Adversarial Learning for Joint Distribution Matching: https://papers.nips.cc/paper/7133-towards-understanding-adversarial-learning-for-joint-distribution-matching
• Finite sample analysis of the GTD Policy Evaluation Algorithms in Markov Setting: https://papers.nips.cc/paper/7134-finite-sample-analysis-of-the-gtd-policy-evaluation-algorithms-in-markov-setting
• On the Complexity of Learning Neural Networks: https://papers.nips.cc/paper/7135-on-the-complexity-of-learning-neural-networks
• Hierarchical Implicit Models and Likelihood-Free Variational Inference: https://papers.nips.cc/paper/7136-hierarchical-implicit-models-and-likelihood-free-variational-inference
• Improved Semi-supervised Learning with GANs using Manifold Invariances: https://papers.nips.cc/paper/7137-improved-semi-supervised-learning-with-gans-using-manifold-invariances
• Approximation and Convergence Properties of Generative Adversarial Learning: https://papers.nips.cc/paper/7138-approximation-and-convergence-properties-of-generative-adversarial-learning
• From Bayesian Sparsity to Gated Recurrent Nets: https://papers.nips.cc/paper/7139-from-bayesian-sparsity-to-gated-recurrent-nets
• Min-Max Propagation: https://papers.nips.cc/paper/7140-min-max-propagation
• What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?: https://papers.nips.cc/paper/7141-what-uncertainties-do-we-need-in-bayesian-deep-learning-for-computer-vision
• Gradient descent GAN optimization is locally stable: https://papers.nips.cc/paper/7142-gradient-descent-gan-optimization-is-locally-stable
• Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks: https://papers.nips.cc/paper/7143-toward-robustness-against-label-noise-in-training-deep-discriminative-neural-networks
• Dualing GANs: https://papers.nips.cc/paper/7144-dualing-gans
• Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model: https://papers.nips.cc/paper/7145-deep-learning-for-precipitation-nowcasting-a-benchmark-and-a-new-model
• Do Deep Neural Networks Suffer from Crowding?: https://papers.nips.cc/paper/7146-do-deep-neural-networks-suffer-from-crowding
• Learning from Complementary Labels: https://papers.nips.cc/paper/7147-learning-from-complementary-labels
• More powerful and flexible rules for online FDR control with memory and weights: https://papers.nips.cc/paper/7148-more-powerful-and-flexible-rules-for-online-fdr-control-with-memory-and-weights
• Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes: https://papers.nips.cc/paper/7149-learning-from-uncertain-curves-the-2-wasserstein-metric-for-gaussian-processes
• Discriminative State Space Models: https://papers.nips.cc/paper/7150-discriminative-state-space-models
• On Fairness and Calibration: https://papers.nips.cc/paper/7151-on-fairness-and-calibration
• Imagination-Augmented Agents for Deep Reinforcement Learning: https://papers.nips.cc/paper/7152-imagination-augmented-agents-for-deep-reinforcement-learning
• Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations: https://papers.nips.cc/paper/7153-extracting-low-dimensional-dynamics-from-multiple-large-scale-neural-population-recordings-by-learning-to-predict-correlations
• Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning: https://papers.nips.cc/paper/7154-unifying-pac-and-regret-uniform-pac-bounds-for-episodic-reinforcement-learning
• Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra: https://papers.nips.cc/paper/7155-gradients-of-generative-models-for-improved-discriminative-analysis-of-tandem-mass-spectra
• Asynchronous Parallel Coordinate Minimization for MAP Inference: https://papers.nips.cc/paper/7156-asynchronous-parallel-coordinate-minimization-for-map-inference
• Multiscale Quantization for Fast Similarity Search: https://papers.nips.cc/paper/7157-multiscale-quantization-for-fast-similarity-search
• Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space: https://papers.nips.cc/paper/7158-diverse-and-accurate-image-description-using-a-variational-auto-encoder-with-an-additive-gaussian-encoding-space
• Improved Training of Wasserstein GANs: https://papers.nips.cc/paper/7159-improved-training-of-wasserstein-gans
• Optimally Learning Populations of Parameters: https://papers.nips.cc/paper/7160-optimally-learning-populations-of-parameters
• Clustering with Noisy Queries: https://papers.nips.cc/paper/7161-clustering-with-noisy-queries
• Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods: https://papers.nips.cc/paper/7162-higher-order-total-variation-classes-on-grids-minimax-theory-and-trend-filtering-methods
• Training Quantized Nets: A Deeper Understanding: https://papers.nips.cc/paper/7163-training-quantized-nets-a-deeper-understanding
• Permutation-based Causal Inference Algorithms with Interventions: https://papers.nips.cc/paper/7164-permutation-based-causal-inference-algorithms-with-interventions
• Time-dependent spatially varying graphical models, with application to brain fMRI data analysis: https://papers.nips.cc/paper/7165-time-dependent-spatially-varying-graphical-models-with-application-to-brain-fmri-data-analysis
• Gradient Methods for Submodular Maximization: https://papers.nips.cc/paper/7166-gradient-methods-for-submodular-maximization
• Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization: https://papers.nips.cc/paper/7167-smooth-primal-dual-coordinate-descent-algorithms-for-nonsmooth-convex-optimization
• Maximizing the Spread of Influence from Training Data: https://papers.nips.cc/paper/7168-maximizing-the-spread-of-influence-from-training-data
• Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos: https://papers.nips.cc/paper/7169-multiplicative-weights-update-with-constant-step-size-in-congestion-games-convergence-limit-cycles-and-chaos
• Learning Neural Representations of Human Cognition across Many fMRI Studies: https://papers.nips.cc/paper/7170-learning-neural-representations-of-human-cognition-across-many-fmri-studies
• A KL-LUCB algorithm for Large-Scale Crowdsourcing: https://papers.nips.cc/paper/7171-a-kl-lucb-algorithm-for-large-scale-crowdsourcing
• Collaborative Deep Learning in Fixed Topology Networks: https://papers.nips.cc/paper/7172-collaborative-deep-learning-in-fixed-topology-networks
• Fast-Slow Recurrent Neural Networks: https://papers.nips.cc/paper/7173-fast-slow-recurrent-neural-networks
• Learning Disentangled Representations with Semi-Supervised Deep Generative Models: https://papers.nips.cc/paper/7174-learning-disentangled-representations-with-semi-supervised-deep-generative-models
• Learning to Generalize Intrinsic Images with a Structured Disentangling Autoencoder: https://papers.nips.cc/paper/7175-learning-to-generalize-intrinsic-images-with-a-structured-disentangling-autoencoder
• Exploring Generalization in Deep Learning: https://papers.nips.cc/paper/7176-exploring-generalization-in-deep-learning
• A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control: https://papers.nips.cc/paper/7177-a-framework-for-multi-armedbandit-testing-with-online-fdr-control
• Fader Networks: Generating Image Variations by Sliding Attribute Values: https://papers.nips.cc/paper/7178-fader-networks-generating-image-variations-by-sliding-attribute-values
• Action Centered Contextual Bandits: https://papers.nips.cc/paper/7179-action-centered-contextual-bandits
• Estimating Mutual Information for Discrete-Continuous Mixtures: https://papers.nips.cc/paper/7180-estimating-mutual-information-for-discrete-continuous-mixtures
• Attention is All you Need: https://papers.nips.cc/paper/7181-attention-is-all-you-need
• Recurrent Ladder Networks: https://papers.nips.cc/paper/7182-recurrent-ladder-networks
• Parameter-Free Online Learning via Model Selection: https://papers.nips.cc/paper/7183-parameter-free-online-learning-via-model-selection
• Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction: https://papers.nips.cc/paper/7184-bregman-divergence-for-stochastic-variance-reduction-saddle-point-and-adversarial-prediction
• Unbounded cache model for online language modeling with open vocabulary: https://papers.nips.cc/paper/7185-unbounded-cache-model-for-online-language-modeling-with-open-vocabulary
• Predictive State Recurrent Neural Networks: https://papers.nips.cc/paper/7186-predictive-state-recurrent-neural-networks
• Early stopping for kernel boosting algorithms: A general analysis with localized complexities: https://papers.nips.cc/paper/7187-early-stopping-for-kernel-boosting-algorithms-a-general-analysis-with-localized-complexities
• SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement: https://papers.nips.cc/paper/7188-svcca-singular-vector-canonical-correlation-analysis-for-deep-understanding-and-improvement
• Convolutional Phase Retrieval: https://papers.nips.cc/paper/7189-convolutional-phase-retrieval
• Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein’s Lemma: https://papers.nips.cc/paper/7190-estimating-high-dimensional-non-gaussian-multiple-index-models-via-steins-lemma
• Gaussian Quadrature for Kernel Features: https://papers.nips.cc/paper/7191-gaussian-quadrature-for-kernel-features
• Value Prediction Network: https://papers.nips.cc/paper/7192-value-prediction-network
• On Learning Errors of Structured Prediction with Approximate Inference: https://papers.nips.cc/paper/7193-on-learning-errors-of-structured-prediction-with-approximate-inference
• Efficient Second-Order Online Kernel Learning with Adaptive Embedding: https://papers.nips.cc/paper/7194-efficient-second-order-online-kernel-learning-with-adaptive-embedding
• Implicit Regularization in Matrix Factorization: https://papers.nips.cc/paper/7195-implicit-regularization-in-matrix-factorization
• Optimal Shrinkage of Singular Values Under Random Data Contamination: https://papers.nips.cc/paper/7196-optimal-shrinkage-of-singular-values-under-random-data-contamination
• Delayed Mirror Descent in Continuous Games: https://papers.nips.cc/paper/7197-delayed-mirror-descent-in-continuous-games
• Asynchronous Coordinate Descent under More Realistic Assumptions: https://papers.nips.cc/paper/7198-asynchronous-coordinate-descent-under-more-realistic-assumptions
• Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls: https://papers.nips.cc/paper/7199-linear-convergence-of-a-frank-wolfe-type-algorithm-over-trace-norm-balls
• Hierarchical Clustering Beyond the Worst-Case: https://papers.nips.cc/paper/7200-hierarchical-clustering-beyond-the-worst-case
• Invariance and Stability of Deep Convolutional Representations: https://papers.nips.cc/paper/7201-invariance-and-stability-of-deep-convolutional-representations
• Statistical Cost Sharing: https://papers.nips.cc/paper/7202-statistical-cost-sharing
• The Expressive Power of Neural Networks: A View from the Width: https://papers.nips.cc/paper/7203-the-expressive-power-of-neural-networks-a-view-from-the-width
• Spectrally-normalized margin bounds for neural networks: https://papers.nips.cc/paper/7204-spectrally-normalized-margin-bounds-for-neural-networks
• Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes: https://papers.nips.cc/paper/7205-robust-and-efficient-transfer-learning-with-hidden-parameter-markov-decision-processes
• Population Matching Discrepancy and Applications in Deep Learning: https://papers.nips.cc/paper/7206-population-matching-discrepancy-and-applications-in-deep-learning
• Scalable Planning with Tensorflow for Hybrid Nonlinear Domains: https://papers.nips.cc/paper/7207-scalable-planning-with-tensorflow-for-hybrid-nonlinear-domains
• Boltzmann Exploration Done Right: https://papers.nips.cc/paper/7208-boltzmann-exploration-done-right
• Towards the ImageNet-CNN of NLP: Pretraining Sentence Encoders with Machine Translation: https://papers.nips.cc/paper/7209-towards-the-imagenet-cnn-of-nlp-pretraining-sentence-encoders-with-machine-translation
• Neural Discrete Representation Learning: https://papers.nips.cc/paper/7210-neural-discrete-representation-learning
• Generalizing GANs: A Turing Perspective: https://papers.nips.cc/paper/7211-generalizing-gans-a-turing-perspective
• Scalable Log Determinants for Gaussian Process Kernel Learning: https://papers.nips.cc/paper/7212-scalable-log-determinants-for-gaussian-process-kernel-learning
• Poincaré Embeddings for Learning Hierarchical Representations: https://papers.nips.cc/paper/7213-poincare-embeddings-for-learning-hierarchical-representations
• Learning Combinatorial Optimization Algorithms over Graphs: https://papers.nips.cc/paper/7214-learning-combinatorial-optimization-algorithms-over-graphs
• Robust Conditional Probabilities: https://papers.nips.cc/paper/7215-robust-conditional-probabilities
• Learning with Bandit Feedback in Potential Games: https://papers.nips.cc/paper/7216-learning-with-bandit-feedback-in-potential-games
• Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments: https://papers.nips.cc/paper/7217-multi-agent-actor-critic-for-mixed-cooperative-competitive-environments
• Communication-Efficient Distributed Learning of Discrete Distributions: https://papers.nips.cc/paper/7218-communication-efficient-distributed-learning-of-discrete-distributions
• Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles: https://papers.nips.cc/paper/7219-simple-and-scalable-predictive-uncertainty-estimation-using-deep-ensembles
• When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness: https://papers.nips.cc/paper/7220-when-worlds-collide-integrating-different-counterfactual-assumptions-in-fairness
• Matrix Norm Estimation from a Few Entries: https://papers.nips.cc/paper/7221-matrix-norm-estimation-from-a-few-entries
• Deep Networks for Decoding Natural Images from Retinal Signals: https://papers.nips.cc/paper/7222-deep-networks-for-decoding-natural-images-from-retinal-signals
• Causal Effect Inference with Deep Latent Variable Models: https://papers.nips.cc/paper/7223-causal-effect-inference-with-deep-latent-variable-models
• Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity: https://papers.nips.cc/paper/7224-learning-identifiable-gaussian-bayesian-networks-in-polynomial-time-and-sample-complexity
• Gradient Episodic Memory for Continuum Learning: https://papers.nips.cc/paper/7225-gradient-episodic-memory-for-continuum-learning
• Radon Machines: Effective Parallelisation for Machine Learning: https://papers.nips.cc/paper/7226-radon-machines-effective-parallelisation-for-machine-learning
• Semisupervised Clustering, AND-Queries and Locally Encodable Source Coding: https://papers.nips.cc/paper/7227-semisupervised-clustering-and-queries-and-locally-encodable-source-coding
• Clustering Stable Instances of Euclidean k-means.: https://papers.nips.cc/paper/7228-clustering-stable-instances-of-euclidean-k-means
• Good Semi-supervised Learning That Requires a Bad GAN: https://papers.nips.cc/paper/7229-good-semi-supervised-learning-that-requires-a-bad-gan
• On Blackbox Backpropagation and Jacobian Sensing: https://papers.nips.cc/paper/7230-on-blackbox-backpropagation-and-jacobian-sensing
• Protein Interface Prediction using Graph Convolutional Networks: https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks
• Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities: https://papers.nips.cc/paper/7232-solid-harmonic-wavelet-scattering-predicting-quantum-molecular-energy-from-invariant-descriptors-of-3d-electronic-densities
• Towards Generalization and Simplicity in Continuous Control: https://papers.nips.cc/paper/7233-towards-generalization-and-simplicity-in-continuous-control
• Random Projection Filter Bank for Time Series Data: https://papers.nips.cc/paper/7234-random-projection-filter-bank-for-time-series-data
• Filtering Variational Objectives: https://papers.nips.cc/paper/7235-filtering-variational-objectives
• On Frank-Wolfe and Equilibrium Computation: https://papers.nips.cc/paper/7236-on-frank-wolfe-and-equilibrium-computation
• Modulating early visual processing by language: https://papers.nips.cc/paper/7237-modulating-early-visual-processing-by-language
• Learning Mixture of Gaussians with Streaming Data: https://papers.nips.cc/paper/7238-learning-mixture-of-gaussians-with-streaming-data
• Practical Hash Functions for Similarity Estimation and Dimensionality Reduction: https://papers.nips.cc/paper/7239-practical-hash-functions-for-similarity-estimation-and-dimensionality-reduction
• Two Time-Scale Update Rule for Generative Adversarial Nets: https://papers.nips.cc/paper/7240-two-time-scale-update-rule-for-generative-adversarial-nets
• The Scaling Limit of High-Dimensional Online Independent Component Analysis: https://papers.nips.cc/paper/7241-the-scaling-limit-of-high-dimensional-online-independent-component-analysis
• Approximation Algorithms for \ell_0-Low Rank Approximation: https://papers.nips.cc/paper/7242-approximation-algorithms-for-ell_0-low-rank-approximation
• The power of absolute discounting: all-dimensional distribution estimation: https://papers.nips.cc/paper/7243-the-power-of-absolute-discounting-all-dimensional-distribution-estimation
• Supervised Adversarial Domain Adaptation: https://papers.nips.cc/paper/7244-supervised-adversarial-domain-adaptation
• Spectral Mixture Kernels for Multi-Output Gaussian Processes: https://papers.nips.cc/paper/7245-spectral-mixture-kernels-for-multi-output-gaussian-processes
• Neural Expectation Maximization: https://papers.nips.cc/paper/7246-neural-expectation-maximization
• Online Learning of Linear Dynamical Systems: https://papers.nips.cc/paper/7247-online-learning-of-linear-dynamical-systems
• Z-Forcing: Training Stochastic Recurrent Networks: https://papers.nips.cc/paper/7248-z-forcing-training-stochastic-recurrent-networks
• Thalamus Gated Recurrent Modules: https://papers.nips.cc/paper/7249-thalamus-gated-recurrent-modules
• Neural Variational Inference and Learning in Undirected Graphical Models: https://papers.nips.cc/paper/7250-neural-variational-inference-and-learning-in-undirected-graphical-models
• Subspace Clustering via Tangent Cones: https://papers.nips.cc/paper/7251-subspace-clustering-via-tangent-cones
• The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process: https://papers.nips.cc/paper/7252-the-neural-hawkes-process-a-neurally-self-modulating-multivariate-point-process
• Inverse Reward Design: https://papers.nips.cc/paper/7253-inverse-reward-design
• Structured Bayesian Pruning via Log-Normal Multiplicative Noise: https://papers.nips.cc/paper/7254-structured-bayesian-pruning-via-log-normal-multiplicative-noise
• Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin: https://papers.nips.cc/paper/7255-attend-and-predict-understanding-gene-regulation-by-selective-attention-on-chromatin
• Acceleration and Averaging in Stochastic Descent Dynamics: https://papers.nips.cc/paper/7256-acceleration-and-averaging-in-stochastic-descent-dynamics
• Kernel functions based on triplet comparisons: https://papers.nips.cc/paper/7257-kernel-functions-based-on-triplet-comparisons
• An Error Detection and Correction Framework for Connectomics: https://papers.nips.cc/paper/7258-an-error-detection-and-correction-framework-for-connectomics
• Style Transfer from Non-parallel Text by Cross-Alignment: https://papers.nips.cc/paper/7259-style-transfer-from-non-parallel-text-by-cross-alignment
• Cross-Spectral Factor Analysis: https://papers.nips.cc/paper/7260-cross-spectral-factor-analysis
• Stochastic Submodular Maximization: The Case of Coverage Functions: https://papers.nips.cc/paper/7261-stochastic-submodular-maximization-the-case-of-coverage-functions
• On Distributed Hierarchical Clustering: https://papers.nips.cc/paper/7262-on-distributed-hierarchical-clustering
• Unsupervised Transformation Learning via Convex Relaxations: https://papers.nips.cc/paper/7263-unsupervised-transformation-learning-via-convex-relaxations
• A Sharp Error Analysis for the Fused Lasso, with Implications to Broader Settings and Approximate Screening: https://papers.nips.cc/paper/7264-a-sharp-error-analysis-for-the-fused-lasso-with-implications-to-broader-settings-and-approximate-screening
• Efficient Computation of Moments in Sum-Product Networks: https://papers.nips.cc/paper/7265-efficient-computation-of-moments-in-sum-product-networks
• A Meta-Learning Perspective on Cold-Start Recommendations for Items: https://papers.nips.cc/paper/7266-a-meta-learning-perspective-on-cold-start-recommendations-for-items
• Predicting Scene Parsing and Motion Dynamics in the Future: https://papers.nips.cc/paper/7267-predicting-scene-parsing-and-motion-dynamics-in-the-future
• Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference: https://papers.nips.cc/paper/7268-sticking-the-landing-simple-lower-variance-gradient-estimators-for-variational-inference
• Efficient Approximation Algorithms for Strings Kernel Based Sequence Classification: https://papers.nips.cc/paper/7269-efficient-approximation-algorithms-for-strings-kernel-based-sequence-classification
• Kernel Feature Selection via Conditional Covariance Minimization: https://papers.nips.cc/paper/7270-kernel-feature-selection-via-conditional-covariance-minimization
• Statistical Convergence Analysis of Gradient EM on General Gaussian Mixture Models: https://papers.nips.cc/paper/7271-statistical-convergence-analysis-of-gradient-em-on-general-gaussian-mixture-models
• Real Time Image Saliency for Black Box Classifiers: https://papers.nips.cc/paper/7272-real-time-image-saliency-for-black-box-classifiers
• Houdini: Democratizing Adversarial Examples: https://papers.nips.cc/paper/7273-houdini-democratizing-adversarial-examples
• Efficient and Flexible Inference for Stochastic Systems: https://papers.nips.cc/paper/7274-efficient-and-flexible-inference-for-stochastic-systems
• When Cyclic Coordinate Descent Beats Randomized Coordinate Descent: https://papers.nips.cc/paper/7275-when-cyclic-coordinate-descent-beats-randomized-coordinate-descent
• Active Learning from Peers: https://papers.nips.cc/paper/7276-active-learning-from-peers
• Learning Causal Graphs with Latent Variables: https://papers.nips.cc/paper/7277-learning-causal-graphs-with-latent-variables
• Learning to Model the Tail: https://papers.nips.cc/paper/7278-learning-to-model-the-tail
• Stochastic Mirror Descent for Non-Convex Optimization: https://papers.nips.cc/paper/7279-stochastic-mirror-descent-for-non-convex-optimization
• On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models: https://papers.nips.cc/paper/7280-on-separability-of-loss-functions-and-revisiting-discriminative-vs-generative-models
• Maxing and Ranking with Few Assumptions: https://papers.nips.cc/paper/7281-maxing-and-ranking-with-few-assumptions
• On clustering network-valued data: https://papers.nips.cc/paper/7282-on-clustering-network-valued-data
• A General Framework for Robust Interactive Learning: https://papers.nips.cc/paper/7283-a-general-framework-for-robust-interactive-learning
• Multi-view Matrix Factorization for Linear Dynamical System Estimation: https://papers.nips.cc/paper/7284-multi-view-matrix-factorization-for-linear-dynamical-system-estimation