2019

Gregory D. Hager, Ann W. Drobnis, Fei Fang, Rayid Ghani, Amy Greenwald, Terah Lyons, David C. Parkes, Jason Schultz, Suchi Saria, Stephen F. Smith, and Milind Tambe. 2019. Artificial Intelligence for Social Good. Download

David C. Parkes and Rakesh V. Vohra. 2019. Algorithmic and Economic Perspectives on Fairness. 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).

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. 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. Download

Haris Aziz, Hau Chan, Barton E. Lee, and David C. Parkes. 2019. “The Capacity Constrained Facility Location Problem.” In Proceedings of the 15th Int. Conf. on Web and Internet Economics, (WINE 2019), Pp. 336. Download

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

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

Jack Serrino, Max Kleiman-Weiner, David C. Parkes, and Joshua B. Tenenbaum. 2019. “Finding Friend and Foe in Multi-Agent Games.” In Proceedings of the 32nd Annual Conf. on Neural Information Processing Systems 2019, (NeurIPS 2019), Pp. 1249-1259. Download

Zhe Feng, David C. Parkes, and Haifeng Xu. 2019. “The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation”. Download

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

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

Berk Ustun, Yang Liu, and David C. Parkes. 2019. “Fairness without Harm: Decoupled Classifiers with Preference Guarantees.” In Proceedings of the 36th International Conference on Machine Learning (ICML'19), Pp. 6373-6382. 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

Hongyao Ma, Fei Fang, and David C. Parkes. 2019. “Spatio-Temporal Pricing for Ridesharing Platforms.” In Proceedings of the 20th ACM Conference on Economics and Computation (EC'19), Pp. 583. Download