Zhe Feng, David C. Parkes, and Haifeng Xu. 2019. “The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation”. Download
Sophie Hilgard, Nir Rosenfeld, Mahzarin R. Banaji, Jack Cao, and David C. Parkes. 2019. “Learning Representations by Humans, for Humans.” In NeurIPS Workshop on Human-Centric Machine Learning.
Goran Radanovic, Rati Devidze, David Parkes, and Adish Singla. 2019. “Learning to Collaborate in Markov Decision Processes.” In Proceedings of the 36th International Conference on Machine Learning (ICML'19), Pp. 5261-5270. Download
Iyad Rahwan, Manuel Cebrian, Nick Obradovich, Josh Bongard, Jean-François Bonnefon, Cynthia Breazeal, Jacob W. Crandall, Nicholas A. Christakis, Iain D. Couzin, Matthew O. Jackson, Nicholas R. Jennings, Ece Kamar, Isabel M. Kloumann, Hugo Larochelle, David Lazer, Richard McElreath, Alan Mislove, David C. Parkes, Alex ‘Sandy’ Pentland, Margaret E. Roberts, Azim Shariff, Joshua B. Tenenbaum, and Michael Wellman. 2019. “Machine behaviour.” Nature, 568, Pp. 477-486. Download
Paul Duetting, Zhe Feng, Noah Golowich, Harikrishna Narasimhan, David C. Parkes, and Sai Srivatsa Ravindranath. 2019. “Machine Learning for Optimal Economic Design.” In The Future of Economic Design, Pp. 495-515. Springer.
Zhe Feng, Okke Schrijvers, and Eric Sodomka. 2019. “Online Learning for Measuring Incentive Compatibility in Ad Auctions.” In Proceeding of The Web Conference 2019, Pp. 2729-2735. Download
Paul Duetting, Zhe Feng, Harikrishna Narasimham, David C. Parkes, and Sai S. Ravindranath. 2019. “Optimal Auctions through Deep Learning.” In Proceedings of the 36th International Conference on Machine Learning (ICML'19), Pp. 1706-1715. Download
Ben Green and Yiling Chen. 2019. “The Principles and Limits of Algorithm-in-the-Loop Decision Making.” Proceedings of the ACM on Human-Computer Interaction, 3, CSCW, Pp. Article 50. Download
Yiling Chen and Shuran Zheng. 2019. “Prior-free Data Acquisition for Accurate Statistical Estimation.” In Proceedings of the 20th ACM Conference on Economics and Computation (EC'19). Download
Yiling Chen, Yang Liu, and Juntao Wang. 2019. “Randomized Wagering Mechanisms.” In Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) . Download
Sarah Keren, Gopal K. Vashishtha, and David C. Parkes. 2019. “Reinforcement Learning Design.” In ICAPS 2019 Workshop on Reasoning about Actions and Processes: Highlights of Recent Advances (RAC-ICAPS 2019).
Duncan Rheingans-Yoo, Scott Duke Kominers, Hongyao Ma, and David C. Parkes. 2019. “Ridesharing with Driver Location Preferences.” In Proceedings of the 28th Int. Joint Conf. on Artificial Intelligence, (IJCAI 2019), Pp. 557-564. Download
Zhe Feng and Jinglai Li. 5/8/2018. “An adaptive independence sampler MCMC algorithm for infinite dimensional Bayesian inferences.” SIAM Journal on Scientific Computing, 40, 3, Pp. 1301-1321. Download
David C. Parkes. 1/2018. “Technical perspective: Moving spectrum.” Communications of the ACM, 61, 1, Pp. 96. Download
Eric Balkanski and Yaron Singer. 2018. “Approximation Guarantees for Adaptive Sampling.” In Proceedings of the 35th International Conference on Machine Learning (ICML 2018).
Dimitris Fotakis, Kyriakos Lotidis, and Chara Podimata. 2018. “A Bridge between Liquid and Social Welfare in Combinatorial Auctions with Submodular Bidders”. Download
Lily Hu. 2018. “Justice Beyond Utility in Artificial Intelligence.” Artificial Intelligence, Ethics, and Society. (AIES 2018). Download
Alexander Spangher and Berk Ustun. 2018. “Actionable Recourse in Linear Classification.” In 5th Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML 2018). Download
Lily Hu, Bryan Wilder, Amulya Yadav, Eric Rice, and Milind Tambe. 2018. “Activating the 'Breakfast Club': Modeling Influence Spread in Natural-World Social Networks.” In Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems. (AAMAS 2018). Download
Shuran Zheng, Bo Waggoner, Yang Liu, and Yiling Chen. 2018. “Active Information Acquisition for Linear Optimization.” In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI-2018). Download