2017

2018
Paul Duetting, Felix A. Fischer, and David C. Parkes. 2018. “Expressiveness and Robustness of First-Price Position Auctions.” Mathematics of Operations Research . Download
2017
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, 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 Auctions through Deep Learning.” ArXiv e-prints. 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
Ming Yin. 2017. “Peeking into the On-Demand Economy: Crowd Behavior and Incentive Design”. 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
Eric Balkanski and Yaron Singer. 2017. “The Sample Complexity of Optimizing a Convex Function.” In Proc. of the Conference on Learning Theory (COLT-17). Download
Yang Liu and Yiling Chen. 2017. “Sequential Peer Prediction: Learning to Elicit Effort using Posted Prices.” In Proc. of The Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) . Download

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