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#DateTitlePresentersInstructor Mentor
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13/29/2016IntroYY
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23/31/2016MW / OL with expertsSZ
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34/5/2016Online Convex OptimizationEllen Feldman, Gautam Goel, Milan CvitkovicYisong
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44/7/2016Multi-armed Bandits & UCB1 AlgorithmHoang Le, Connor Lee, Ritvik MishraHoang
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54/12/2016Linear Bandits & ApplicationsPengchuan Zheng, Feng Bi, Leiya Ma, Joon Sik KimYisong
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64/14/2016Monte Carlo Tree Search, GoSuraj Nair, Peter Kundzicz, Vansh Kumar, Kevin AnStephan
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74/19/2016Reinforcement Learning, (Atari or Memory Controller)Timothy Chou, Charlie Tong, Vincent ZhuangStephan
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84/21/2016
Reinforcement Learning via Apprenticeship Learning, Helicopter
Nick Haliday, Audrey Huang, Dryden Bouamalay, Ritwik AnandHoang
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94/26/2016Imitation LearningRichard Zhu, Andrew Kang, Dimitar HoHoang
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104/28/2016Active Learning for Supervised LearningDaniel Gu, Matthew Morgan, Keegan Ryan, Matthew ClarkHoang
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115/3/2016Active Learning for Decision MakingJoe Marino, Grant Van Horn, Alvita TranYisong
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125/5/2016CrowdsourcingSreenivas Appasani, Madhav Mohandas, Ajay MandlekarYisong
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135/10/2016Machine TeachingJustin Leong, Kevin Tang, Zilong Chen, Kaikai ShengYisong
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145/12/2016Machine Teaching for CrowdsourcingNancy Cao, Andrew Chico, Betsy FuYisong
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155/17/2016Modeling Human Decision MakingZachary Fein, Eric Gorlin, Emily MazoHoang
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165/19/2016Combinatorial Action Spaces, Adaptive RoutingLuciana Cendon, Tobias Bischoff, Jiyun Ivy Xiao, Brennan YoungYisong
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175/24/2016Dueling BanditsFabian Boemer, Kushal Agarwal, Jialin Song, Aman AgarwalYisong
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185/26/2016Coactive LearningRohan Batra, Avishek Dutta, Nand Kishore, Siddharth MurchingHoang
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195/31/2016Bayesian OptimizationErya Yu, Danni MaHoang
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206/2/2016Off-Policy EvaluationMiguel Aroca-Ouellette, Akshata Athawale, Mannat SinghStephan
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Available topicsReferences (also see website for more)
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(Dueling Bandits)
The K-armed Dueling Bandits Problem, by Yisong Yue, Josef Broder, Robert Kleinberg, and Thorsten Joachims. Journal of Computer and System Sciences, DOI:10.1016/j.jcss.2011.12.028, 2012.
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(Coactive Learning)
Online Structured Prediction via Coactive Learning, by Pannaga Shivaswamy and Thorsten Joachims. International Conference on Machine Learning, 2012. [journal version]
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(Active Learning for Decision Making)
Near Optimal Bayesian Active Learning for Decision Making, by Shervin Javdani, Yuxin Chen, Amin Karbasi, Andreas Krause, Drew Bagnell, Siddhartha Srinivasa. International Conference on Artificial International and Statistics, 2014.
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(Bayesian Optimization)
Practical Bayesian Optimization of Machine Learning Algorithms, by Jasper Snoek, Hugo Larochelle, and Ryan Adams. Neural Information Processing Systems, 2012.
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(Off-Policy Evaluation)
Exploration Scavenging, by John Langford, Alexander Strehl, and Jenn Wortman Vaughan. International Conference on Machine Learning, 2008.
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(Imitation Learning)
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning, by Stephane Ross, Geoff Gordon, and Drew Bagnell. International Conference on Artificial Intelligence and Statistics, 2011.
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(Reinforcement Learning, Monte Carlo Tree Search)
A Survey of Monte Carlo Tree Search Methods by Cameron Browne, Edward Powley, Daniel Whitehouse, Simon Lucas, Peter I. Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon Samothrakis and Simon Colton. IEEE Transactions on Computational Intelligence and AI in Games, 4(1), 2012.
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(Crowdsourcing)
Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing, by Xi Chen, Qihang Lin, and Denny Zhou. International Conference on Machine Learning, 2013. [appendix][journal version]
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(Machine Teaching)
How Do Humans Teach: On Curriculum Learning and Teaching Dimension, by Faisal Khan, Xiaojin Zhu, and Bilge Mutlu. Neural Information Processing Systems, 2011.
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(Modeling Human Decision Making)
Forgetful Bayes and myopic planning: Human learning and decision-making in a bandit setting, by Shunan Zhang and Angela Yu. Neural Information Processing Systems, 2013.
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(Combinatorial Action Spaces, Adaptive Routing)
Non-Myopic Adaptive Route Planning in Uncertain Congestion Environments, by Siyuan Liu, Yisong Yue, and Ramayya Krishnan. ACM Transactions on Knowledge Discovery and Engineering, DOI 10.1109/TKDE.2015.2411278, 2015.
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(Reinforcement Learning from via Apprenticeship Learning)
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(Active Learning for Supervised Learning)Importance Weighted Active Learning
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(Machine Teaching for Crowdsourcing)Near-Optimally Teaching the Crowd to Classify
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