【最新】机器学习顶会 NIPS 2017 Pre-Proceedings 论文列表（附pdf下载链接）- 专知

【最新】机器学习顶会 NIPS 2017 Pre-Proceedings 论文列表（附pdf下载链接）

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【导读】机器学习领域顶尖学术会议——神经信息处理系统进展大会（Advances in NeuralInformation Processing Systems，NIPS），就是放在整个计算机科学界，也是数一数二的顶级学术会议。今年的NIPS将于 12 月份在美国长滩举行，本届NIPS共收到 3240 篇论文投稿，录用 678 篇，录用率为 20.9%；其中包括 40 篇口头报告论文和 112 篇 spotlight 论文。微软共中了16篇论文，其中微软亚洲研究院有4篇，Google有23篇。清华大学，今年共有6篇录用论文，包括张钹院士、王建民博士、鲁继文博士、朱军博士都有论文被录用；而北京大学有四篇论文被录用，中国科学院、中国科学技术大学、香港科技大学、香港中文大学及香港城市大学在内的多家高校也有多篇论文中了NIPS。

▌简介

机器学习领域顶尖学术会议——神经信息处理系统进展大会（Advances in Neural Information ProcessingSystems，NIPS），就是放在整个计算机科学界，也是数一数二的顶级学术会议。为鼓励跨学科研究，NIPS 惯例上除录用机器学习方面的文章外，还会录用一部分神经科学方面的文章，有时甚至多达 1/3。与其他机器学习顶级会议（如国际机器学习会议 (ICML)）相比，NIPS 更偏向于神经网络和贝叶斯方法。但由于其神经科学方面的文章一般达不到相关领域重要期刊论文的水平，而其机器学习方面的文章则达到顶级水平，因此通常认为 NIPS 是一个机器学习方面的顶级会议。NIPS 每篇投稿文章都会收到大约 6 个审稿意见，其中 3 个为详细 (heavy) 意见，另外 3 个为简略 (light) 意见。详细意见就是我们通常看到的审稿意见，包含了对文章优点和缺点较为详细的评论，而简略意见则只需要审稿人针对文章给出一个简单总结。大会认为，简略意见虽然不具体，但其打分也可以为最后的录用决定提供参考。自 2013 年以来，NIPS 大会录用的文章在发表的同时，其审稿意见和作者的回复也将一并在网上发表。

NIPS 2017 将于 12 月份在美国长滩举行，但从很早开始议论就没有停，尤其是围绕论文。本届NIPS共收到 3240 篇论文投稿，录用 678 篇，录用率为 20.9%；其中包括 40 篇口头报告论文和 112 篇 spotlight 论文。详细录用名单日前已经公布，可参见：https://nips.cc/Conferences/2017/AcceptedPapersInitial

微软共中了16篇论文，其中微软亚洲研究院有4篇。Google有23篇，包括之前备受关注的《Attention is All you Need》。Elon Mask投资的OpenAI有三篇。facebook6篇。

国内科研实力最强的清华大学，今年共有6篇录用论文，包括张钹院士、王建民博士、鲁继文博士、朱军博士都有论文被录用；而北京大学有四篇论文被录用。此外，包括中国科学院、中国科学技术大学、香港科技大学、香港中文大学及香港城市大学在内的多家高校也有多篇论文中了NIPS。

CMU 教授 Tuomas Sandholm和其博士生 Noam Brown 获得了 NIPS-17 最佳论文奖，获奖论文为《Safe and Nested Subgame Solving for Imperfect-Information Games》。

▌关键词统计信息：

专知进行关键词统计信息如下：

可以看出相关学习理论，深度学习，神经网络，变分方法，高斯相关方法等等是投稿论文热点。

▌论文列表：

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
Concentration of Multilinear Functions of the Ising Model with Applications to Network Data
Deep Subspace Clustering Network
Attentional Pooling for Action Recognition
On the Consistency of Quick Shift
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization
Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis
Dilated Recurrent Neural Networks
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs
Scalable Generalized Linear Bandits: Online Computation and Hashing
Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning
Interactive Submodular Bandit
Scene Physics Acquisition via Visual De-animation
Label Efficient Learning of Transferable Representations acrosss Domains and Tasks
Decoding with Value Networks for Neural Machine Translation
Parametric Simplex Method for Sparse Learning
Group Sparse Additive Machine
Uprooting and Rerooting Higher-order Graphical Models
The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings
From Parity to Preference: Learning with Cost-effective Notions of Fairness
Inferring Generative Model Structure with Static Analysis
Structured Embedding Models for Grouped Data
A Linear-Time Kernel Goodness-of-Fit Test
Cortical microcircuits as gated-recurrent neural networks
k-Support and Ordered Weighted Sparsity for Overlapping Groups: Hardness and Algorithms
A simple model of recognition and recall memory
On Structured Prediction Theory with Calibrated Convex Surrogate Losses
Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model
MaskRNN: Instance Level Video Object Segmentation
Gated Recurrent Convolution Neural Network for OCR
Towards Accurate Binary Convolutional Neural Network
Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks
Learning a Multi-View Stereo Machine
Phase Transitions in the Pooled Data Problem
Universal Style Transfer via Feature Transforms
On the Model Shrinkage Effect of Gamma Process Edge Partition Models
Pose Guided Person Image Generation
Inference in Graphical Models via Semidefinite Programming Hierarchies
Variable Importance Using Decision Trees
Preventing Gradient Explosions in Gated Recurrent Units
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems
f-GANs in an Information Geometric Nutshell
Multimodal Image-to-Image Translation by Enforcing Bi-Cycle Consistency
Mixture-Rank Matrix Approximation for Collaborative Filtering
Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms
Learning with Average Top-k Loss
Learning multiple visual domains with residual adapters
Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions
Flat2Sphere: Learning Spherical Convolution for Fast Features from 360° Imagery
3D Shape Reconstruction by Modeling 2.5D Sketch
Multimodal Learning and Reasoning for Visual Question Answering
Adversarial Surrogate Losses for Ordinal Regression
Hypothesis Transfer Learning via Transformation Functions
Adversarial Invariant Feature Learning
Convergence Analysis of Two-layer Neural Networks with ReLU Activation
Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks
Efficient Online Linear Optimization with Approximation Algorithms
Geometric Descent Method for Convex Composite Minimization
Diffusion Approximations for Online Principal Component Estimation and Global Convergence
Avoiding Discrimination through Causal Reasoning
Nonparametric Online Regression while Learning the Metric
Recycling for Fairness: Learning with Conditional Distribution Matching Constraints
Safe and Nested Subgame Solving for Imperfect-Information Games
Unsupervised Image-to-Image Translation Networks
Coded Distributed Computing for Inverse Problems
A Screening Rule for l1-Regularized Ising Model Estimation
Improved Dynamic Regret for Non-degeneracy Functions
Learning Efficient Object Detection Models with Knowledge Distillation
One-Sided Unsupervised Domain Mapping
Deep Mean-Shift Priors for Image Restoration
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees
A New Theory for Nonconvex Matrix Completion
Robust Hypothesis Test for Functional Effect with Gaussian Processes
Lower bounds on the robustness to adversarial perturbations
Minimizing a Submodular Function from Samples
Introspective Classification with Convolutional Nets
Label Distribution Learning Forests
Unsupervised object learning from dense equivariant image labelling
Compression-aware Training of Deep Neural Networks
Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces
PredRNN: Recurrent Neural Networks for Video Prediction using Spatiotemporal LSTMs
Detrended Partial Cross Correlation for Brain Connectivity Analysis
Contrastive Learning for Image Captioning
Safe Model-based Reinforcement Learning with Stability Guarantees
Online multiclass boosting
Matching on Balanced Nonlinear Representations for Treatment Effects Estimation
Learning Overcomplete HMMs
GP CaKe: Effective brain connectivity with causal kernels
Decoupling "when to update" from "how to update"
Self-Normalizing Neural Networks
Learning to Pivot with Adversarial Networks
MolecuLeNet: A continuous-filter convolutional neural network for modeling quantum interactions
Active Bias: Training a More Accurate Neural Network by Emphasizing High Variance Samples
Differentiable Learning of Submodular Functions
Inductive Representation Learning on Large Graphs
Subset Selection for Sequential Data
Question Asking as Program Generation
Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces
Gradient Descent Can Take Exponential Time to Escape Saddle Points
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction
One-Shot Imitation Learning
Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding
Integration Methods and Optimization Algorithms
Sharpness, Restart and Acceleration
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data
Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications
Predictive-State Decoders: Encoding the Future into Recurrent Networks
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
Matching neural paths: transfer from recognition to correspondence search
Linearly constrained Gaussian processes
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets
Learning to Inpaint for Image Compression
Adaptive Bayesian Sampling with Monte Carlo EM
No More Fixed Penalty Parameter in ADMM: Faster Convergence with New Adaptive Penalization
Shape and Material from Sound
Flexible statistical inference for mechanistic models of neural dynamics
Online Prediction with Selfish Experts
Tensor Biclustering
DPSCREEN: Dynamic Personalized Screening
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach
Testing and Learning on Distributions with Symmetric Noise Invariance
A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering
Deanonymization in the Bitcoin P2P Network
Accelerated consensus via Min-Sum Splitting
Generalized Linear Model Regression under Distance-to-set Penalties
Adaptive sampling for a population of neurons
Nonbacktracking Bounds on the Influence in Independent Cascade Models
Learning with Feature Evolvable Streams
Online Convex Optimization with Stochastic Constraints
Max-Margin Invariant Features from Transformed Unlabelled Data
Cognitive Impairment Prediction in Alzheimer’s Disease with Regularized Modal Regression
Translation Synchronization via Truncated Least Squares
From which world is your graph
A New Alternating Direction Method for Linear Programming
Regret Analysis for Continuous Dueling Bandit
Best Response Regression
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
Learning Affinity via Spatial Propagation Networks
Linear regression without correspondence
NeuralFDR: Learning Discovery Thresholds from Hypothesis Features
Cost efficient gradient boosting
Probabilistic Rule Realization and Selection
Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions
A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis
Learning Multiple Tasks with Deep Relationship Networks
Deep Hyperalignment
Online to Offline Conversions and Adaptive Minibatch Sizes
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure
Deep Learning with Topological Signatures
Predicting User Activity Level In Point Process Models With Mass Transport Equation
Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues
Deep Dynamic Poisson Factorization Model
Positive-Unlabeled Learning with Non-Negative Risk Estimator
Optimal Sample Complexity of M-wise Data for Top-K Ranking
What-If Reasoning using Counterfactual Gaussian Processes
Communication-Efficient Stochastic Gradient Descent, with Applications to Neural Networks
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
Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks
Model evidence from nonequilibrium simulations
Minimal Exploration in Structured Stochastic Bandits
Learned D-AMP: Principled Neural-network-based Compressive Image Recovery
Deliberation Networks: Sequence Generation Beyond One-Pass Decoding
Adaptive Clustering through Semidefinite Programming
Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning
Repeated Inverse Reinforcement Learning
The Numerics of GANs
Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search
Learning Chordal Markov Networks via Branch and Bound
Revenue Optimization with Approximate Bid Predictions
Solving (Almost) all Systems of Random Quadratic Equations
Unsupervised Learning of Disentangled Latent Representations from Sequential Data
Lookahead Bayesian Optimization with Inequality Constraints
Hierarchical Methods of Moments
Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts
Revisit Fuzzy Neural Network: Demystifying Batch Normalization and ReLU with Generalized Hamming Network
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization
Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models
Generating steganographic images via adversarial training
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
PixelGAN Autoencoders
Consistent Multitask Learning with Nonlinear Output Relations
Fast Alternating Minimization Algorithms for Dictionary Learning
Learning ReLUs via Gradient Descent
Stabilizing Training of Generative Adversarial Networks through Regularization
Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems
Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs
Compatible Reward Inverse Reinforcement Learning
First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization
Hiding Images in Plain Sight: Deep Steganography
Neural Program Meta-Induction
Bayesian Dyadic Trees and Histograms for Regression
A graph-theoretic approach to multitasking
Consistent Robust Regression
Natural value approximators: learning when to trust past estimates
Bandits Dueling on Partially Ordered Sets
Elementary Symmetric Polynomials for Optimal Experimental Design
Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols
Backprop without Learning Rates Through Coin Betting
Pixels to Graphs by Associative Embedding
Runtime Neural Pruning
Compressing the Gram Matrix for Learning Neural Networks in Polynomial Time
MMD GAN: Towards Deeper Understanding of Moment Matching Network
The Reversible Residual Network: Backpropagation Without Storing Activations
Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe
Zap Q-Learning
Expectation Propagation for t-Exponential Family Using Q-Algebra
Few-Shot Learning Through an Information Retrieval Lens
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation
Associative Embedding: End-to-End Learning for Joint Detection and Grouping
Practical Locally Private Heavy Hitters
Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences
Inhomogoenous Hypergraph Clustering with Applications
Differentiable Learning of Logical Rules for Knowledge Base Reasoning
Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks
Masked Autoregressive Flow for Density Estimation
Non-convex Finite-Sum Optimization Via SCSG Methods
Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting
Inner-loop free ADMM using Auxiliary Deep Neural Networks
OnACID: Online Analysis of Calcium Imaging Data in Real Time
Collaborative PAC Learning
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization
Scalable Demand-Aware Recommendation
SGD Learns the Conjugate Kernel Class of the Network
Noise-Tolerant Interactive Learning Using Pairwise Comparisons
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems
Generative Local Metric Learning for Kernel Regression
Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications
Fitting Low-Rank Tensors in Constant Time
Deep supervised discrete hashing
Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation
How regularization affects the critical points in linear networks
Fisher GAN
Information-theoretic analysis of generalization capability of learning algorithms
Sparse Approximate Conic Hulls
Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems
Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM
EX2: Exploration with Exemplar Models for Deep Reinforcement Learning
Multitask Spectral Learning of Weighted Automata
Multi-way Interacting Regression via Factorization Machines
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
Practical Data-Dependent Metric Compression with Provable Guarantees
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models
Nonlinear random matrix theory for deep learning
Parallel Streaming Wasserstein Barycenters
ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games
Dual Discriminator Generative Adversarial Nets
Dynamic Revenue Sharing
Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search
Multi-agent Predictive Modeling with Attentional CommNets
An Empirical Bayes Approach to Optimizing Machine Learning Algorithms
Differentially Private Empirical Risk Minimization Revisited: Faster and More General
Variational Inference via \chi Upper Bound Minimization
On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
An Empirical Study on The Properties of Random Bases for Kernel Methods
Bridging the Gap Between Value and Policy Based Reinforcement Learning
Premise Selection for Theorem Proving by Deep Graph Embedding
A Bayesian Data Augmentation Approach for Learning Deep Models
Principles of Riemannian Geometry in Neural Networks
Cold-Start Reinforcement Learning with Softmax Policy Gradients
Online Dynamic Programming
Alternating Estimation for Structured High-Dimensional Multi-Response Models
Convolutional Gaussian Processes
Estimation of the covariance structure of heavy-tailed distributions
Mean Field Residual Networks: On the Edge of Chaos
Decomposable Submodular Function Minimization: Discrete and Continuous
Gauging Variational Inference
Deep Recurrent Neural Network-Based Identification of Precursor microRNAs
Robust Estimation of Neural Signals in Calcium Imaging
State Aware Imitation Learning
Beyond Parity: Fairness Objectives for Collaborative Filtering
A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent
Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach
Model-Powered Conditional Independence Test
Deep Voice 2: Multi-Speaker Neural Text-to-Speech
Variance-based Regularization with Convex Objectives
Deep Lattice Networks and Partial Monotonic Functions
Continual Learning with Deep Generative Replay
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms
Learning Causal Structures Using Regression Invariance
Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback
Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem
Reinforcement Learning under Model Mismatch
Hierarchical Attentive Recurrent Tracking
Tomography of the London Underground: a Scalable Model for Origin-Destination Data
Rotting Bandits
Unbiased estimates for linear regression via volume sampling
An Applied Algorithmic Foundation for Hierarchical Clustering
Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition
Stein Variational Gradient Descent as Gradient Flow
Partial Hard Thresholding: A Towards Unified Analysis of Support Recovery
Shallow Updates for Deep Reinforcement Learning
A Highly Efficient Gradient Boosting Decision Tree
Adversarial Ranking for Language Generation
Regret Minimization in MDPs with Options without Prior Knowledge
Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee
Graph Matching via Multiplicative Update Algorithm
Dynamic Importance Sampling for Anytime Bounds of the Partition Function
Is the Bellman residual a bad proxy?
Generalization Properties of Learning with Random Features
Differentially private Bayesian learning on distributed data
Learning to Compose Domain-Specific Transformations for Data Augmentation
Wasserstein Learning of Deep Generative Point Process Models
Ensemble Sampling
Language modeling with recurrent highway hypernetworks
Searching in the Dark: Practical SVRG Methods under Error Bound Conditions with Guarantee
Bayesian Compression for Deep Learning
Streaming Sparse Gaussian Process Approximations
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
Sparse k-Means Embedding
Utile Context Tree Weighting
A Regularized Framework for Sparse and Structured Neural Attention
Multi-output Polynomial Networks and Factorization Machines
Clustering Billions of Reads for DNA Data Storage
Multi-Objective Non-parametric Sequential Prediction
A Universal Analysis of Large-Scale Regularized Least Squares Solutions
Deep Sets
ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
Process-constrained batch Bayesian optimisation
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
Spherical convolutions and their application in molecular modelling
Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding
On Optimal Generalizability in Parametric Learning
Near Optimal Sketching of Low-Rank Tensor Regression
Tractability in Structured Probability Spaces
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
Neural system identification for large populations separating "what" and "where"
Certified Defenses for Data Poisoning Attacks
Eigen-Distortions of Hierarchical Representations
Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization
Unsupervised Sequence Classification using Sequential Output Statistics
Subset Selection under Noise
Collecting Telemetry Data Privately
Concrete Dropout
Adaptive Batch Size for Safe Policy Gradients
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
Bayesian GANs
Off-policy evaluation for slate recommendation
A multi-agent reinforcement learning model of common-pool resource appropriation
On the Optimization Landscape of Tensor Decompositions
High-Order Attention Models for Visual Question Answering
Sparse convolutional coding for neuronal assembly detection
Quantifying how much sensory information in a neural code is relevant for behavior
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Reducing Reparameterization Gradient Variance
Visual Reference Resolution using Attention Memory for Visual Dialog
Joint distribution optimal transportation for domain adaptation
Multiresolution Kernel Approximation for Gaussian Process Regression
Collapsed variational Bayes for Markov jump processes
Universal consistency and minimax rates for online Mondrian Forest
Efficiency Guarantees from Data
Diving into the shallows: a computational perspective on large-scale shallow learning
End-to-end Differentiable Proving
Influence Maximization with \varepsilon-Almost Submodular Threshold Function
Inferring The Latent Structure of Human Decision-Making from Raw Visual Inputs
Variational Laws of Visual Attention for Dynamic Scenes
Recursive Sampling for the Nystrom Method
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning
Dynamic Routing Between Capsules
Incorporating Side Information by Adaptive Convolution
Conic Scan Coverage algorithm for nonparametric topic modeling
FALKON: An Optimal Large Scale Kernel Method
Structured Generative Adversarial Networks
Conservative Contextual Linear Bandits
Variational Memory Addressing in Generative Models
On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm
Scalable Levy Process Priors for Spectral Kernel Learning
Deep Hyperspherical Learning
Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction
On-the-fly Operation Batching in Dynamic Computation Graphs
Nonlinear Acceleration of Stochastic Algorithms
Optimized Pre-Processing for Discrimination Prevention
YASS: Yet Another Spike Sorter
Independence clustering (without a matrix)
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders
Adaptive Active Hypothesis Testing under Limited Information
Streaming Weak Submodularity: Interpreting Neural Networks on the Fly
Successor Features for Transfer in Reinforcement Learning
Counterfactual Fairness
Prototypical Networks for Few-shot Learning
Triple Generative Adversarial Nets
Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression
Mapping distinct timescales of functional interactions among brain networks
Multi-Armed Bandits with Metric Movement Costs
Learning A Structured Optimal Bipartite Graph for Co-Clustering
Learning Low-Dimensional Metrics
The Marginal Value of Adaptive Gradient Methods in Machine Learning
Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification
Deconvolutional Paragraph Representation Learning
Random Permutation Online Isotonic Regression
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
Inverse Filtering for Hidden Markov Models
Non-parametric Neural Networks
Learning Active Learning from Data
VAE Learning via Stein Variational Gradient Descent
Deep adversarial neural decoding
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
Sobolev Training for Neural Networks
Multi-Information Source Optimization
Deep Reinforcement Learning from Human Preferences
On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks
Policy Gradient With Value Function Approximation For Collective Multiagent Planning
Adversarial Symmetric Variational Autoencoder
Tensor encoding and decomposition of brain connectomes with application to tractography evaluation
A Minimax Optimal Algorithm for Crowdsourcing
Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach
A Decomposition of Forecast Error in Prediction Markets
Safe Adaptive Importance Sampling
Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net
Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication
Unsupervised Learning of Disentangled Representations from Video
Federated Multi-Task Learning
Is Input Sparsity Time Possible for Kernel Low-Rank Approximation?
The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities
Improved Graph Laplacian via Geometric Self-Consistency
Dual Path Networks
Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers
A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks
DisTraL: Robust multitask reinforcement learning
Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions
Trimmed Density Ratio Estimation
Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems
Visual Interaction Networks
Reconstruct & Crush Network
Streaming Robust Submodular Maximization:A Partitioned Thresholding Approach
Simple strategies for recovering inner products from coarsely quantized random projections
Discovering Potential Influence via Information Bottleneck
Doubly Stochastic Variational Inference for Deep Gaussian Processes
Ranking Data with Continuous Labels through Oriented Recursive Partitions
Scalable Model Selection for Belief Networks
Targeting EEG/LFP Synchrony with Neural Nets
Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs
Non-Stationary Spectral Kernels
Overcoming Catastrophic Forgetting by Incremental Moment Matching
Balancing information exposure in social networks
SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud
Query Complexity of Clustering with Side Information
QMDP-Net: Deep Learning for Planning under Partial Observability
Robust Optimization for Non-Convex Objectives
Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation
Adaptive Classification for Prediction Under a Budget
Convergence rates of a partition based Bayesian multivariate density estimation method
Affine-Invariant Online Optimization
Beyond Worst-case: A Probabilistic Analysis of Affine Policies in Dynamic Optimization
A unified approach to interpreting model predictions
Stochastic Approximation for Canonical Correlation Analysis
Investigating the learning dynamics of deep neural networks using random matrix theory
Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions
Scalable Variational Inference for Dynamical Systems
Context Selection for Embedding Models
Working hard to know your neighbor's margins: Local descriptor learning loss
Accelerated Stochastic Greedy Coordinate Descent by Soft Thresholding Projection onto Simplex
Multi-Task Learning for Contextual Bandits
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon
Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds
Selective Classification for Deep Neural Networks
Minimax Estimation of Bandable Precision Matrices
Monte-Carlo Tree Search by Best Arm Identification
Group Additive Structure Identification for Kernel Nonparametric Regression
Fast, Sample-Efficient Algorithms for Structured Phase Retrieval
Hash Embeddings for Efficient Word Representations
Online Learning for Multivariate Hawkes Processes
Maximum Margin Interval Trees
DropoutNet: Addressing Cold Start in Recommender Systems
A simple neural network module for relational reasoning
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes
Online Reinforcement Learning in Stochastic Games
Position-based Multiple-play Multi-armed Bandit Problem with Unknown Position Bias
Active Exploration for Learning Symbolic Representations
Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling
Fair Clustering Through Fairlets
Polynomial time algorithms for dual volume sampling
Hindsight Experience Replay
Stochastic and Adversarial Online Learning without Hyperparameters
Teaching Machines to Describe Images with Natural Language Feedback
Perturbative Black Box Variational Inference
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization
Learning Graph Embeddings with Embedding Propagation
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
A-NICE-MC: Adversarial Training for MCMC
Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models
Real-Time Bidding with Side Information
Saliency-based Sequential Image Attention with Multiset Prediction
Variational Inference for Gaussian Process Models with Linear Complexity
K-Medoids For K-Means Seeding
Identifying Outlier Arms in Multi-Armed Bandit
Online Learning with Transductive Regret
Riemannian approach to batch normalization
Self-supervised Learning of Motion Capture
Triangle Generative Adversarial Networks
Preserving Proximity and Global Ranking for Node Embedding
Bayesian Optimization with Gradients
Second-order Optimization in Deep Reinforcement Learning using Kronecker-factored Approximation
Renyi Differential Privacy Mechanisms for Posterior Sampling
Online Learning with a Hint
Identification of Gaussian Process State Space Models
Robust Imitation of Diverse Behaviors
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
Local Aggregative Games
A Sample Complexity Measure with Applications to Learning Optimal Auctions
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
Improving the Expected Improvement Algorithm
Hybrid Reward Architecture for Reinforcement Learning
Approximate Supermodularity Bounds for Experimental Design
Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification
AdaGAN: Boosting Generative Models
Straggler Mitigation in Distributed Optimization Through Data Encoding
Multi-View Decision Processes
A Greedy Approach for Budgeted Maximum Inner Product Search
SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks
Plan, Attend, Generate: Planning for Sequence-to-Sequence Models
Task-based End-to-end Model Learning in Stochastic Optimization
Towards Understanding Adversarial Learning for Joint Distribution Matching
Finite sample analysis of the GTD Policy Evaluation Algorithms in Markov Setting
On the Complexity of Learning Neural Networks
Hierarchical Implicit Models and Likelihood-Free Variational Inference
Improved Semi-supervised Learning with GANs using Manifold Invariances
Approximation and Convergence Properties of Generative Adversarial Learning
From Bayesian Sparsity to Gated Recurrent Nets
Min-Max Propagation
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
Gradient descent GAN optimization is locally stable
Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks
Dualing GANs
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
Do Deep Neural Networks Suffer from Crowding?
Learning from Complementary Labels
More powerful and flexible rules for online FDR control with memory and weights
Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes
Discriminative State Space Models
On Fairness and Calibration
Imagination-Augmented Agents for Deep Reinforcement Learning
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
Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra
Asynchronous Parallel Coordinate Minimization for MAP Inference
Multiscale Quantization for Fast Similarity Search
Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space
Improved Training of Wasserstein GANs
Optimally Learning Populations of Parameters
Clustering with Noisy Queries
Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods
Training Quantized Nets: A Deeper Understanding
Permutation-based Causal Inference Algorithms with Interventions
Time-dependent spatially varying graphical models, with application to brain fMRI data analysis
Gradient Methods for Submodular Maximization
Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization
Maximizing the Spread of Influence from Training Data
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
A KL-LUCB algorithm for Large-Scale Crowdsourcing
Collaborative Deep Learning in Fixed Topology Networks
Fast-Slow Recurrent Neural Networks
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
Learning to Generalize Intrinsic Images with a Structured Disentangling Autoencoder
Exploring Generalization in Deep Learning
A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control
Fader Networks: Generating Image Variations by Sliding Attribute Values
Action Centered Contextual Bandits
Estimating Mutual Information for Discrete-Continuous Mixtures
Attention is All you Need
Recurrent Ladder Networks
Parameter-Free Online Learning via Model Selection
Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction
Unbounded cache model for online language modeling with open vocabulary
Predictive State Recurrent Neural Networks
Early stopping for kernel boosting algorithms: A general analysis with localized complexities
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement
Convolutional Phase Retrieval
Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein’s Lemma
Gaussian Quadrature for Kernel Features
Value Prediction Network
On Learning Errors of Structured Prediction with Approximate Inference
Efficient Second-Order Online Kernel Learning with Adaptive Embedding
Implicit Regularization in Matrix Factorization
Optimal Shrinkage of Singular Values Under Random Data Contamination
Delayed Mirror Descent in Continuous Games
Asynchronous Coordinate Descent under More Realistic Assumptions
Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls
Hierarchical Clustering Beyond the Worst-Case
Invariance and Stability of Deep Convolutional Representations
Statistical Cost Sharing
The Expressive Power of Neural Networks: A View from the Width
Spectrally-normalized margin bounds for neural networks
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes
Population Matching Discrepancy and Applications in Deep Learning
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains
Boltzmann Exploration Done Right
Towards the ImageNet-CNN of NLP: Pretraining Sentence Encoders with Machine Translation
Neural Discrete Representation Learning
Generalizing GANs: A Turing Perspective
Scalable Log Determinants for Gaussian Process Kernel Learning
Poincaré Embeddings for Learning Hierarchical Representations
Learning Combinatorial Optimization Algorithms over Graphs
Robust Conditional Probabilities
Learning with Bandit Feedback in Potential Games
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Communication-Efficient Distributed Learning of Discrete Distributions
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness
Matrix Norm Estimation from a Few Entries
Deep Networks for Decoding Natural Images from Retinal Signals
Causal Effect Inference with Deep Latent Variable Models
Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity
Gradient Episodic Memory for Continuum Learning
Radon Machines: Effective Parallelisation for Machine Learning
Semisupervised Clustering, AND-Queries and Locally Encodable Source Coding
Clustering Stable Instances of Euclidean k-means.
Good Semi-supervised Learning That Requires a Bad GAN
On Blackbox Backpropagation and Jacobian Sensing
Protein Interface Prediction using Graph Convolutional Networks
Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities
Towards Generalization and Simplicity in Continuous Control
Random Projection Filter Bank for Time Series Data
Filtering Variational Objectives
On Frank-Wolfe and Equilibrium Computation
Modulating early visual processing by language
Learning Mixture of Gaussians with Streaming Data
Practical Hash Functions for Similarity Estimation and Dimensionality Reduction
Two Time-Scale Update Rule for Generative Adversarial Nets
The Scaling Limit of High-Dimensional Online Independent Component Analysis
Approximation Algorithms for \ell_0-Low Rank Approximation
The power of absolute discounting: all-dimensional distribution estimation
Supervised Adversarial Domain Adaptation
Spectral Mixture Kernels for Multi-Output Gaussian Processes
Neural Expectation Maximization
Online Learning of Linear Dynamical Systems
Z-Forcing: Training Stochastic Recurrent Networks
Thalamus Gated Recurrent Modules
Neural Variational Inference and Learning in Undirected Graphical Models
Subspace Clustering via Tangent Cones
The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process
Inverse Reward Design
Structured Bayesian Pruning via Log-Normal Multiplicative Noise
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin
Acceleration and Averaging in Stochastic Descent Dynamics
Kernel functions based on triplet comparisons
An Error Detection and Correction Framework for Connectomics
Style Transfer from Non-parallel Text by Cross-Alignment
Cross-Spectral Factor Analysis
Stochastic Submodular Maximization: The Case of Coverage Functions
On Distributed Hierarchical Clustering
Unsupervised Transformation Learning via Convex Relaxations
A Sharp Error Analysis for the Fused Lasso, with Implications to Broader Settings and Approximate Screening
Efficient Computation of Moments in Sum-Product Networks
A Meta-Learning Perspective on Cold-Start Recommendations for Items
Predicting Scene Parsing and Motion Dynamics in the Future
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference
Efficient Approximation Algorithms for Strings Kernel Based Sequence Classification
Kernel Feature Selection via Conditional Covariance Minimization
Statistical Convergence Analysis of Gradient EM on General Gaussian Mixture Models
Real Time Image Saliency for Black Box Classifiers
Houdini: Democratizing Adversarial Examples
Efficient and Flexible Inference for Stochastic Systems
When Cyclic Coordinate Descent Beats Randomized Coordinate Descent
Active Learning from Peers
Learning Causal Graphs with Latent Variables
Learning to Model the Tail
Stochastic Mirror Descent for Non-Convex Optimization
On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models
Maxing and Ranking with Few Assumptions
On clustering network-valued data
A General Framework for Robust Interactive Learning
Multi-view Matrix Factorization for Linear Dynamical System Estimation