Publications

2022
Gianluca Brero, Nicolas Lepore, Eric Mibuari, and David C. Parkes. 2022. “Learning to Mitigate AI Collusion on Economic Platforms.” In https://arxiv.org/abs/2202.07106 . NeurIPS 2022.
Zhe Feng, David C. Parkes, and Sai Srivatsa Ravindranath. 2022. “Machine Learning for Matching Markets.” In Online matching theory and market design. N. Immorlica, F. Echenique, and V. Vazirani (eds), Cambridge University Press.
Rediet Abebe, Hubert Chan, Jon Kleinberg, Zhibin Liang, David C. Parkes, Mauro Sozio, and Charalampos Tsourakakis. 2022. “Opinion Dynamics with Varying Susceptibility to Persuasion via Non-Convex Local.” ACM Transactions on Knowledge Discovery from Data, 16, 2, Pp. 33:1-33:34 .
Hongyao Ma, Fei Fang, and David C. Parkes. 2022. “Spatio-temporal pricing for ridesharing platforms.” Operations Research , 70, 2, Pp. 1025-1041.
Francesca Dominici, Elizabeth Langdon-Gray, and David C. Parkes. 2022. “Spinning Up a Data Science Initiative at Harvard.” Harvard Data Science Review, 4, 4.
2021
Paul Tylkin, Goran Radanovic, and David C. Parkes. 2021. “Learning Robust Helpful Behaviors in Two-Player Cooperative Atari Environments.” In Proc. 20th Int. Conf. on Auton. Agents and Multiagent Systems (AAMAS), Pp. 1686-1688 .
Matheus V. X. Ferreira, Daniel J. Moroz, David C. Parkes, and Mitchell Stern. 2021. “Dynamic posted-price mechanisms for the blockchain transaction-fee market.” In AFT 2021, Pp. 86-99.
Mark York, Munther Dahleh, and David C. Parkes. 2021. “Eliciting Social Knowledge for Creditworthiness Assessment.” In Proc. 17th Conference on Web and Internet Economics , Pp. 428-445.
David C. Parkes and Francesca Dominici. 2021. “Introducing the Interim Co-Editors-in-Chief.” PubPub.
Sophie Hilgard, Nir Rosenfeld, Mahzarin R. Banaji, Jack Cao, and David C. Parkes. 2021. “Learning Representations by Humans.” In ICML 2021, Pp. 4227-4238.
Gianluca Brero, Darshan Chakrabarti, Alon Eden, Matthias Gerstgrasser, Vincent Li, and David C. Parkes. 2021. “Learning Stackelberg Equilibria in Sequential Price Mechanisms.” In . Proc. ICML Workshop for Reinforcement Learning Theory.
Paul Duetting, Zhe Feng, Hari Narasimhan, David C. Parkes, and Sai Srivatsa Ravindranath. 2021. “Optimal auctions through deep learning.” Communications of the ACM, 64, 8, Pp. 109-116.
David Parkes. 2021. “Playing with symmetry with neural networks.” Nature Machine Intelligence , 3, 8, Pp. 658-658.
Gianluca Brero, Alon Eden, Matthias Gerstgrasser, David C. Parkes, and Duncan Rheingans-Yoo. 2021. “Reinforcement Learning of Simple Indirect Mechanisms.” In The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), Pp. 5219-5227.
Sarah A. Wu, Rose E. Wang, James A. Evans, Joshua B. Tenenbaum, David C. Parkes, and Max Kleiman-Weiner. 2021. “Too Many Cooks: Bayesian Inference for Coordinating Multi-Agent Collaboration.” Topics Cognitive Science , 13, 2, Pp. 2032-2034.
Vincent Conitzer, Zhe Feng, David C. Parkes, and Eric Sodomka. 2021. “Welfare-Preserving ε-BIC to BIC Transformation with Negligible Revenue Loss.” In , Pp. 76-94. Proceedings 17th Conference on Web and Internet Economics (WINE).
2020
Michael Neuder, Daniel J. Moroz, Rithvik Rao, and David C. Parkes. 2020. “Selfish Behavior in the Tezos Proof-of-Stake Protocol.” In Cryptoeconomic Systems (CES) Conference 2020.
Sarah A. Wu, Rose E. Wang, James A. Evans, Josh Tenenbaum, David C. Parkes, and Max Kleiman-Weiner. 2020. “Too many cooks: Coordinating multi-agent collaboration through inverse planning.” In Proc. 42nd Annual Meeting of the Cognitive Science Society, Pp. 889-895 .
Haris Aziz, Hau Chan, Barton E. Lee, and David C. Parkes. 2020. “The capacity constrained facility location problem.” Games Econ. Behavior, 124:478–490, Pp. 478-490.
Michael Neuder, Daniel J. Moroz, Rithvik Rao, and David C. Parkes. 2020. “Defending against malicious reorgs in tezos proof-of-stake.” In AFT ’20: 2nd ACM Conference on Advances in Financial Technologies (ACM ’20), Pp. 46–58.

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