Parkes, David C.
Bibliographic References tagged with Parkes, David C.
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L. D’Amico-Wong, Y. A. Gonczarowski, G. Q. Ma, and D. C. Parkes,
“Disrupting Bipartite Trading Networks: Matching for Revenue Maximization.”, in Proc. Twenty-Fifth ACM Conference on Economics and Computation (EC’24)., 2024.
L. D’Amico-Wong, Y. A. Gonczarowski, G. Q. Ma, and D. C. Parkes,
“Disrupting Bipartite Trading Networks: Matching for Revenue Maximization.”, in Proc. Twenty-Fifth ACM Conference on Economics and Computation (EC’24)., 2024.
P. Duetting, Z. Feng, H. Narasimhan, D. C. Parkes, and S. S. Ravindranath,
“Optimal Auctions through Deep Learning: Advances in Differentiable Economics. ”, J. ACM , vol. 71, no. (1), pp. 5:1–5:53, 2024.
P. Duetting, Z. Feng, H. Narasimhan, D. C. Parkes, and S. S. Ravindranath,
“Optimal Auctions through Deep Learning: Advances in Differentiable Economics. ”, J. ACM , vol. 71, no. (1), pp. 5:1–5:53, 2024.
H. Sun, Z. Deng, H. Chen, and D. C. Parkes,
“Decision-Aware Conditional GANs for Time Series Data.”, in ICAIF 2023, 2023, pp. 36–45.
H. Sun, Z. Deng, H. Chen, and D. C. Parkes,
“Decision-Aware Conditional GANs for Time Series Data.”, in ICAIF 2023, 2023, pp. 36–45.
M. Gerstgrasser and D. C. Parkes,
“Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning.”, in ICML 2023, 2023, pp. 11213–11236.
M. Gerstgrasser and D. C. Parkes,
“Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning.”, in ICML 2023, 2023, pp. 11213–11236.
S. Fish et al.,
“Generative Social Choice”, Proc. 25th Conf. on Economics and Computation (EC’24), 2024.
S. Fish et al.,
“Generative Social Choice”, Proc. 25th Conf. on Economics and Computation (EC’24), 2024.
A. Eden, G. Q. Ma, and D. C. Parkes,
“Platform Equilibrium: Analyzing Social Welfare in Online Market Places.”, in Proc. 25th Conf. on Economics and Computation (EC’24) , 2024.
A. Eden, G. Q. Ma, and D. C. Parkes,
“Platform Equilibrium: Analyzing Social Welfare in Online Market Places.”, in Proc. 25th Conf. on Economics and Computation (EC’24) , 2024.
S. S. Ravindranath, Y. Jiang, and D. C. Parkes,
“ Data Market Design through Deep Learning.”, in NeurIPS 2023, 2023.
S. S. Ravindranath, Y. Jiang, and D. C. Parkes,
“ Data Market Design through Deep Learning.”, in NeurIPS 2023, 2023.
T. Wang, P. Duetting, D. Ivanov, I. Talgam-Cohen, and D. C. Parkes,
“Deep Contract Design via Discontinuous Networks.”, in Proceedings of the NeurIPS 2023 (Thirty-seventh Conference on Neural Information Processing Systems), 2023.
T. Wang, P. Duetting, D. Ivanov, I. Talgam-Cohen, and D. C. Parkes,
“Deep Contract Design via Discontinuous Networks.”, in Proceedings of the NeurIPS 2023 (Thirty-seventh Conference on Neural Information Processing Systems), 2023.
D. Goktas et al.,
“Generative Adversarial Equilibrium Solvers.”, in The Twelfth International Conference on Learning Representations, ICLR 2024., 2024.
D. Goktas et al.,
“Generative Adversarial Equilibrium Solvers.”, in The Twelfth International Conference on Learning Representations, ICLR 2024., 2024.
O. Nahum, G. Noti, D. C. Parkes, and N. Rosenfeld,
“Decongestion by Representation: Learning to Improve Economic Welfare in Marketplaces.”, in The Twelfth International Conference on Learning Representations, ICLR 2024., 2024.
O. Nahum, G. Noti, D. C. Parkes, and N. Rosenfeld,
“Decongestion by Representation: Learning to Improve Economic Welfare in Marketplaces.”, in The Twelfth International Conference on Learning Representations, ICLR 2024., 2024.