Ben Green. 4/2019. The Smart Enough City, Putting Technology in It's Place to Reclaim Our Urban Future., Pp. 240. Cambridge: MIT Press. Download
Ben Green and Yiling Chen. 1/2019. “Disparate Interactions: An Algorithm-in-the-Loop Analysis of Fairness in Risk Assessments.” In Conference on Fairness, Accountability, and Transparency (FAT* ’19). 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
Berk Ustun, Alexander Spanghler, and Yang Liu. 2019. “Actionable Recourse in Linear Classification.” In ACM Conference on Fairness, Accountability, and Transparency (FAT '19). Download
David C. Parkes and Rakesh V. Vohra. 2019. Algorithmic and Economic Perspectives on Fairness. Download
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
Christos Dimitrakakis, Yang Liu, David C. Parkes, and Goran Radanovic. 2019. “Bayesian Fairness.” In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI). 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
Hongyao Ma, Reshef Meir, David C. Parkes, and James Zou. 2019. “Contingent Payment Mechanisms for Resource Utilization.” In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), Pp. 422-430. Download
Eric Balkanski, Aviad Rubinstein, and Yaron Singer. 2019. “An Exponential Speedup in Parallel Running Time for Submodular Maximization without Loss in Approximation.” In Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA) 2019 . 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
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
Nripsuta Saxena, Karen Huang, Evan DeFilippis, Goran Radanovic, David Parkes, and Yang Liu. 2019. “How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness.” In Proceedings of AI Ethics and Society (AIES) , Pp. 99-106. Download
Thanh H. Nguyen and Haifeng Xu. 2019. “Imitative Attacker Deception in Stackelberg Security Games.” In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019). Download
Jiarui Gan, Haifeng Xu, Qingyu Guo, Long Tran-Thanh, Zinovi Rabinovich, and Michael Wooldridge. 2019. “Imitative Follower Deception in Stackelberg Games.” In Proceedings of the 20th ACM Conference on Economics and Computation (EC'19). Download
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. 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. 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