Machine Learning

Working Paper
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).
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) .
2023
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).
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.
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 .
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
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), Pp. 5219-5227.
2020
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.
2019
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.

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