Yang Liu, Goran Radanovic, Christos Dimitrakakis, Debmalya Mandal, and David C. Parkes. 2017. “
Calibrated fairness in Bandits.” In Proceedings of the 4th Workshop on Fairness, Accountability, and Transparency in Machine Learning (Fat/ML 2017).
Download Reshef Meir, Hongyao Ma, and Valentin Robu. 2017. “
Contract Design for Energy Demand Response.” In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17.
Download Rediet Abebe, Jon M. Kleinberg, and David C. Parkes. 2017. “
Fair Division via Social Comparison.” In Proc. 16th Conf. on Autonomous Agents and Multiagent Systems (AAMAS'17), Pp. 281-289.
Download Yang Liu, Goran Radanovic, Christos Dimitrakakis, Debmalya Mandal, and David C. Parkes. 2017. “
Fair Experimentation.” In Conference on Digital Experimentation (CODE).
Yang Liu. 2017. “
Fair Optimal Stopping Policy for Matching with Mediator.” In Proc. of the Conference on Uncertainty in Artificial Intelligence (UAI-2017).
Download Lily Hu and Yiling Chen. 2017. “
Fairness at Equilibrium in the Labor Market.” In Workshop on Fairness, Accountability, and Transparency in Machine Learning. (FAT/ML 2017).
Download Boi Faltings and Goran Radanovic. 2017.
Game Theory for Data Science: Eliciting Truthful Information. Morgan & Claypool Publishers.
Hongyao Ma, David C. Parkes, and Valentin Robu. 2017. “
Generalizing Demand Response Through Reward Bidding.” In Proc. of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS).
Download Benjamin Lubin, Adam I. Juda, Ruggiero Cavallo, Sebastien Lahaie, Jeffrey Shneidman, and David C. Parkes. 2017. “
ICE: An Expressive Iterative Combinatorial Exchange.” In Handbook of Spectrum Auction Design, 39: Pp. 828-873. Cambidge University Press.
Eric Balkanski, Nicole Immorlica, and Yaron Singer. 2017. “
The Importance of Communities for Learning to Influence.” In Proceedings of the Conference on Neural Information Processing Systems (NIPS) 2017.
Download Eric Balkanski, Aviad Rubinstein, and Yaron Singer. 2017. “
The Limitations of Optimization from Samples.” In ACM Symposium on the Theory of Computing (STOC 2017).
Download Liu Yang and Yiling Chen. 2017. “
Machine Learning aided Peer Prediction.” In Proceedings of the 18th ACM Conference on Economics and Computation (EC-2017).
Download Christos Dimitrakakis, David C. Parkes, Goran Radanovic, and Paul Tylkin. 2017. “
Multi-View Decision Processes: The Helper-AI Problem.” In Proc. 30th Advances in Neural Information Processing Systems (NIPS'17), Pp. 5449-5458.
Download Paul Duetting, Zhe Feng, Harikrishna Narasimhan, and David C. Parkes. 2017. “
Optimal Economic Design through Deep Learning.” In Proc. of the NPS Workshop on "Learning in the Presence of Strategic Behavior." Long Beach, CA.
Download Arpit Agarwal, Debmalya Mandal, David C. Parkes, and Nisarg Shah. 2017. “
Peer Prediction with Heterogeneous Users.” In Proceedings of the 18th ACM Conference on Economics and Computation (EC-2017).
Download Hassidim Avinatan and Yaron Singer. 2017. “
Robust Guarantees of Stochastic Greedy Algorithms.” In International Conference of Machine Learning (ICML) .
Download Robert Chen, Brendan Lucier, Vasilis Syrgkanis, and Yaron Singer. 2017. “
Robust Optimization for Non-Convex Objectives.” In Proceedings of the Conference on Neural Information Processing Systems (NIPS) 2017.
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