Stephan Zheng, Alexander Trott, Sunil Srinivasa, Richard Socher, and David C. Parkes. Working Paper. “The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning.” CoRR abs/2004.13332.

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.

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.

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 .

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.

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).

Nir Rosenfeld, Anna Hilgard, Sai S. Ravindranath, and David C. Parkes. 2020. “From predictions to decisions: Using lookahead regularization.” In In Annual Conference on Neural Information Processing Systems, NeurIPS ’20.

Zhe Feng, David C. Parkes, and Haifeng Xu. 2020. “The intrinsic robustness of stochastic bandits to strategic manipulation.” In Proceedings of the 37th International Conference on Machine Learning, ICML ’20, 119: Pp. 3092-3101. Proceedings of Machine Learning Research.

Paul Tylkin, Goran Radanovic, and David C. Parkes. 2020. “Learning Robust Helpful Behaviors in Two-Player Cooperative Atari Environments.” In NeurIPS 2020 workshop on Cooperative AI.

Paul Duetting, Zhe Feng, Harikrishna Narasimhan, David C. Parkes, and Sai S. Ravindranath. 2020. “Optimal auctions through deep learning.” Communications of the ACM, 63, 12.

Arpit Agarwal, Debmalya Mandal, David C. Parkes, and Nisarg Shah. 2020. “Peer Prediction with Heterogeneous Users.” ACM Transactions on Economics and Computation, 8, 1, Pp. 2:1-2:34.

Gianluca Brero, Alon Eden, Matthias Gerstgrasser, David C. Parkes, and Duncan Rheingans-Yoo. 2020. “Reinforcement learning of simple indirect mechanisms.” In NeurIPS’20 Workshop on Machine learning for Economic Policy.

Berk Ustun, Yang Liu, and David C. Parkes. 2019. “Fairness without Harm: Decoupled Classifiers with Preference Guarantees.” In Proceedings of the 36th International Conference on Machine Learning (ICML’19), Pp. 6373-6382.

Sophie Hilgard, Nir Rosenfeld, Mahzarin R. Banaji, Jack Cao, and David C. Parkes. 2019. “Learning Representations by Humans, for Humans.” In NeurIPS Workshop on Human-Centric Machine Learning.

Goran Radanovic, Rati Devidze, David Parkes, and Adish Singla. 2019. “Learning to Collaborate in Markov Decision Processes.” In Proceedings of the 36th International Conference on Machine Learning (ICML’19), Pp. 5261-5270.

Paul Duetting, Zhe Feng, Harikrishna Narasimham, David C. Parkes, and Sai S. Ravindranath. 2019. “Optimal Auctions through Deep Learning.” In Proceedings of the 36th International Conference on Machine Learning (ICML’19), Pp. 1706-1715.

Noah Golowich, Harikrishna Narasimhan, and David C. Parkes. 2018. “Deep Learning for Multi-Facility Location Mechanism Design.” In Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI 2018), Pp. 261-267.

Zhe Feng, Harikrishna Narasimhan, and David C. Parkes. 2018. “Deep Learning for Revenue-Optimal Auctions with Budgets.” In Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems. (AAMAS 2018), Pp. 354-362.