Publications

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.
2021
Barton E. Lee, Daniel J. Moroz, and David C. Parkes. 2021. ““An Analysis of Blockchain Governance via Political Economics”.
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 .
Hongyao Ma, Reshef Meir, David C. Parkes, and Elena Wu-Yan. 2021. “Penalty Bidding Mechanisms for Allocating Resources and Overcoming Present Bias”.
David Parkes. 2021. “Playing with symmetry with neural networks.” Nature Machine Intelligence .
Hongyao Ma, Fei Fang, and David C. Parkes. 2021. “Spatio-temporal pricing for ridesharing platforms.” Operations Research .
Michael Neuder, Rithvik Rao, Daniel J. Moroz, and David C. Parkes. 2021. “Strategic Liquidity Provision in Uniswap v3 ”.
Stephan Zheng, Alexander Trott, Sunil Srinivasa, Richard Socher, and David C. Parkes. 2021. “The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning.” CoRR abs/2004.13332.
Ignacio Palacios-Huerta, David C. Parkes, and Richard Steinberg. 2021. “Combinatorial Auctions in Practice”.
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”.
Rediet Abebe, Hubert Chan, Jon Kleinberg, Zhibin Liang, David C. Parkes, Mauro Sozio, and Charalampos Tsourakakis. 2021. “Opinion Dynamics with Varying Susceptibility to Persuasion via Non-Convex Local”.
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.
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).
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”.
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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