【KDD2018来袭】31大人工智能前沿Tutorial，百家争鸣，百花齐放- 专知

【KDD2018来袭】31大人工智能前沿Tutorial，百家争鸣，百花齐放

【导读】当地时间 8 月 19 - 23 日，备受关注的人工智能数据挖掘顶级国际会议 KDD 2018在英国伦敦举行。在这次会议上，各大人工智能和机器学习领域的研究者为我们呈现了这一领域的研究前沿内容，其中包括：图挖掘算法、隐私保护、医疗健康AI、社交网络建模、出行零售人工智能等等，呈现出很强的多样性；业界涵盖Google、微软、Linkedin与国内的京东、滴滴出行等等，包括京东的《零售中的人工智能》与滴滴《交通出行中的人工智能》。与此同时，一些资深研究者也带来了一些极具看点和启发价值的演讲和教程，如：来自IBM《基于深度学习的计算医疗健康》，Google的《大规模图算法：理论, 实践》，俄罗斯的《Web服务在线评价策略》，专知整理了今天KDD31个Tutorial介绍

KDD 2018 Conventional Tutorial

以下是专知对下面Tutorials 的简单介绍：

T01. 图与张量挖掘
Graph and Tensor Mining for Fun and Profit
Xin Luna Dong (Amazon), Christos Faloutsos (Amazon and CMU), Andrey Kan (Amazon), Subhbrata Mukherjee (Amazon), and Jun Ma (Amazon)
链接：

http://www.cs.cmu.edu/~christos/TALKS/18-08-KDD-tut/

T02. 工业中保护隐私的数据挖掘:实践中的挑战和经验教训
Privacy-preserving Data Mining in Industry: Practical Challenges and Lessons Learned
Krishnaram Kenthapadi (LinkedIn), Ilya Mironov (Google), Abhradeep Guha Thakurta (UC Santa Cruz)
链接：

https://sites.google.com/view/kdd2018privacytutorial/

T03. 图探索:让我展示一下您的图中相关的内容
Graph Exploration: Let me Show what is Relevant in your Graph
Davide Mottin (HPI), Emmanuel Müller (HPI)
链接：

https://hpi.de//mueller/tutorials/graph-exploration-kdd.html

T04. 事实确证：理论与实践

Fact Checking: Theory and Practice (KDD 2018 Tutorial)

Xin Luna Dong (Amazon), Christos Faloutsos (Amazon and CMU),

Xian Li (Amazon), Subhabrata Mukherjee (Amazon), Prashant

Shiralkar (Amazon)

链接：

https://shiralkarprashant.github.io/fact-checking-tutorial-KDD2018/

T05. 俄罗斯-Web服务的在线评价方法
Online Evaluation for Effective Web Service Development
Roman Budylin (Yandex), Alexey Drutsa (Yandex), Gleb Gusev (Yandex), Pavel Serdyukov (Yandex), Igor Yashkov (Yandex)
链接：

https://research.yandex.com/tutorials/online-evaluation/kdd-2018

T06. 重描述挖掘：理论、算法、应用

Redescription Mining: Theory, Algorithms, and Applications

Esther Galbrun (Aalto University), Pauli Miettinen (Max Planck Institute for Informatics)

链接：

http://siren.mpi-inf.mpg.de/tutorial_kdd2018/

T07. 团队网络科学:描述、预测和优化
Network Science of Teams: Characterization, Prediction, and Optimization
Liangyue Li (Arizona State University), Hanghang Tong (Arizona State University)
链接：

http://www.public.asu.edu/~liangyue/team-tutorial.html

T08. 反歧视学习：从联想到因果
Anti-discrimination Learning: From Association to Causation AI
Lu Zhang (University of Arkansas), Yongkai Wu (University of Arkansas), Xintao Wu (University of Arkansas)
链接：

http://csce.uark.edu/~xintaowu/kdd18-tutorial/

T09. 规模隐私:实践中的局部差异隐私
Privacy at Scale: Local Differential Privacy in Practice
Graham Cormode(University Of Warwick), Tejas Kulkarni (University Of Warwick), Ninghui Li (Purdue University), Tianhao Wang (Purdue University)

链接：

https://sites.google.com/view/kdd2018-tutorial/home

T10. 真实世界交互学习
Real World Interactive Learning

Alekh Agarwal (Microsoft Research)

链接：

http://hunch.net/~rwil/kdd2018.html

T11. Google大规模图算法：理论, 实践
Large-Scale Graph Algorithmics: Theory and Practice Generalizations
Silvio Lattanzi (Google), Vahab Mirrokni (Google)
链接：

https://sites.google.com/corp/view/lsga/home

T12. 绘图、抽样、流式和空间效率优化
Graph Sketching, Sampling, Streaming, and Space-Efficient Optimization
Sudipto Guha (Amazon) and Andrew McGregor (UMass Amherst)
链接：

https://people.cs.umass.edu/~mcgregor/graphs/

T13. 滴滴-交通出行中的人工智能
Artificial Intelligence in Transportation
Zheng Wang (Didi Chuxing), Yan Liu (Didi Chuxing & University of Southern California), Jieping Ye (Didi Chuxing & University of Michigan, Ann Arbor)
链接：

https://outreach.didichuxing.com/tutorial/kdd2018/

T15. 从队列，电子健康记录和进一步的病人相关数据的知识发现
Knowledge Discovery from Cohorts, Electronic Health Records and further Patient-related data
Panagiotis Papapetrou (Stockholm University) and Myra Spiliopoulou (University of Magdeburg)
链接：

http://www.kmd.ovgu.de/KMD+Events/Data+Science+for+Health.html

T16. 社交媒体的两极分化:如何发现和缓解
Polarization in social media: how to detect and mitigate
Aristides Gionis (Aalto University), Michael Mathioudakis (University of Helsinki)
链接：

http://gvrkiran.github.io/polarization/

T17. 因果推理和反事实推断
Causal Inference and Counterfactual Reasoning
Emre Kiciman(Microsoft Research), Amit Sharma (Microsoft Research)
链接：

https://causalinference.gitlab.io/kdd-tutorial/

T18. 图度量空间
Graph Metric Spaces
Jose Bento (Boston College), Tina Eliassi-Rad (Northeastern), and Stratis Ioannidis (Northeastern)
链接：

https://neu-spiral.github.io/GraphMetricSpaces/

T19. 算法地图推理科学
The Science of Algorithmic Map Inference
Favyen Bastani (MIT), Songtao He (MIT), Sofiane Abbar (QCRI), Mohammad Alizadeh (MIT), Sanjay Chawla (QCRI)
链接：

https://sites.google.com/view/algorithmic-map-making/home

T20. 京东-数据科学：零售作为一种服务
Data Science in Retail-as-a-Service Retrieval
Zuo-Jun (Max) Shen (UC Berkeley), Rong Yuan (JD.com), Di Wu (JD.com), Jian Pei (JD.com)
链接：

http://www.aigsic.com/kdd_2018_raas.html

T21. 众包数据挖掘
Crowd-Powered Data Mining
Chengliang Chai(Tsinghua University), Ju Fan(Renmin University), Guoliang Li(Tsinghua University), Jiannan Wang(SFU), Yudian Zheng(Twitter)
链接：

http://dbgroup.cs.tsinghua.edu.cn/ligl/kdd

T22. 行为分析：方法和应用
Behavior Analytics: Methods and Applications
Longbing Cao(University of Technology Sydney), Philip S Yu (University of Illinois at Chicago), Guansong Pang (University of Technology Sydney)
链接：

http://kdd2018tutorial-behavior.datasciences.org/

T23. 深度学习计算医疗
Deep Learning for Computational Healthcare
Edward Choi (Georgia Tech), Cao Xiao (IBM Research) and Jimeng Sun (Georgia Tech)
链接：

http://dl4health.org/

T24. 端到端的目标引导问答系统
End-to-end Goal-oriented Question Answering Systems
Deepak Agarwal (LinkedIn), Bee-Chung Chen (LinkedIn), Qi He (LinkedIn), Mikhail Obukhov (LinkedIn), Jaewon Yang (LinkedIn), Liang Zhang (LinkedIn)
链接：

https://sites.google.com/view/goal-oriented-qa/

T25. 多维度文本数据分析
Towards Multidimensional Analysis of Text Corpora
Jingbo Shang (UIUC), Chao Zhang (UIUC), Jiaming Shen (UIUC), Jiawei Han (UIUC)
链接：
https://shangjingbo1226.github.io/2018-04-21-kdd-tutorial/

T26. 社交网络计算
Computational Models for Social Network Analysis
Yuxiao Dong (Microsoft Research), Jie Tang (Tsinghua University)
链接：

https://aminer.org/kdd18-sna

T27. 医疗AI中可解释模型
Explainable Models for Healthcare AI
Muhammad Aurangzeb Ahmad, Dr. Carly Eckert and Ankur Teredesai (University of Washington)
链接：

https://mlhealthcare.github.io/

T28. 知识图谱构建
Building a Large-scale, Accurate and Fresh Knowledge Graph
Yuqing Gao (Microsoft), Jisheng Liang (Microsoft), Benjamin Han (Microsoft), Mohamed Yakout (Microsoft), Ahmed Mohamed (Microsoft)
链接：

https://satorikdd2018.azurewebsites.net/

T29. 数据科学中的博弈论:获取真实的信息
Game Theory to Data Science: Eliciting Truthful Information
Boi Faltings and Goran Radanovic
链接：

https://lia.epfl.ch/~faltings/ijcai2018_tutorial_web/

T30. 文本提取和推断
Knowledge Extraction and Inference from Text: Shallow, Deep, and Everything in Between
Knowledge Extraction and Inference from Text: Shallow, Deep, and Everything in Between
链接：

https://sites.google.com/site/keit2018kdd/

T31. 网络数据建模

Modeling Data With Networks + Network Embedding: Problems, Methodologies and Frontiers
Peng Cui (Tsinghua University), Jian Pei (SFU), Wenwu Zhu (Tsinghua University), Tanya Berger-Wolf (UIC), Ivan Brugere (UIC) Bryan Perozzi (Google)

链接：

https://ivanbrugere.github.io/kdd2018/

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