Denizalp Goktas, David C. Parkes, Ian Gemp, Luke Marris, Georgios Piliouras, Romuald Elie, Guy Lever, and Andrea Tacchetti. Working Paper. “Generative Adversarial Equilibrium Solvers.” CoRR abs/2302.06607 (2023) .

Omer Nahum, Gali Noti, David C. Parkes, and Nir Rosenfeld. Working Paper. “Decongestion by Representation: Learning to Improve Economic Welfare in Marketplaces.” CoRR abs/2306.10606 (2023).

Sai Srivatsa Ravindranath, Zhe Feng, Shira Li, Jonathan Ma, Scott D. Kominers, and David C. Parkes. Working Paper. “Deep Learning for Two-Sided Matching.” CoRR abs/2107.03427 (2021).

Barton E. Lee, Daniel J. Moroz, and David C. Parkes. Working Paper. “An Analysis of Blockchain Governance via Political Economics.” 2021.

Michael Neuder, Rithvik Rao, Daniel J. Moroz, David C. Parkes, Zhou Fan, and Francisco Marmolejo-Cossío. Working Paper. “Strategic Liquidity Provision in Uniswap v3.” 2023.

Daniel J. Moroz, Daniel J. Aronoff, Neha Narula, and David C. Parkes. Working Paper. “Double-Spend Counterattacks: Threat of Retaliation in Proof-of-Work Systems.” CoRR abs/2002.10736.

Tonghan Wang, Paul Duetting, Dmitry Ivanov, Inbal Talgam-Cohen, and David C. Parkes. 2023. “Deep Contract Design via Discontinuous Networks.” In Proceedings of the NeurIPS 2023 (Thirty-seventh Conference on Neural Information Processing Systems).

Paul Duetting, Felix A. Fischer, and David C. Parkes. 2023. “Non-Truthful Position Auctions Are More Robust to Misspecification.” Mathematics of Operations Research.

Yaniv Yacoby, John Girash, and David C. Parkes. 2023. “Empowering First-Year Computer Science Ph.D. Students to Create a Culture that Values Community and Mental Health.” In Proc. of the The Technical Symposium on Computer Science Education , 1: Pp. 694-700 .

Matheus V. X. Ferreira and David C. Parkes. 2023. “Credible Decentralized Exchange Design via Verifiable Sequencing Rules.” In Proceedings of the 55th Annual ACM Symposium on Theory of Computing (STOC 2023), Pp. 723-736 .

Zhun Deng, He Sun, Steven Wu, Linjun Zhang, and David C. Parkes. 2023. “Reinforcement Learning with Stepwise Fairness Constraints.” In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS) , Pp. 10594-10618 .

Xintong Wang, Gary Qiurui Ma, Alon Eden, Clara Li, Alexander Trott, Stephan Zheng, and David C. Parkes. 2023. “Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study.” In Proc. of the International World Wide Web Conference (WWW '23), Pp. 3592-3602 .

Jamelle Watson-Daniels, David C. Parkes, and Berk Ustun. 2023. “Predictive Multiplicity in Probabilistic Classification.” In Proc. of Association for the Advancement of Artificial Intelligence, Pp. 10306 - 10314. AAAI.

Francesca Dominici, Elizabeth Langdon-Gray, and David C. Parkes. 2022. “Spinning Up a Data Science Initiative at Harvard.” Harvard Data Science Review, 4, 4.

Mira Finkelstein, Nitsan Levy Schlot, Lucy Liu, Yoav Kolumbus, David C. Parkes, Jeffrey S. Rosenschein, and Sarah Keren. 2022. “Explainable Reinforcement Learning via Model Transforms.” In Proceedings of the Conference on Neural Information Processing Systems (NeurIPS) .