Parkes, David C.
Bibliographic References tagged with Parkes, David C.
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M. Finkelstein et al.,
“Explainable Reinforcement Learning via Model Transforms”, in Proceedings of the Conference on Neural Information Processing Systems (NeurIPS) , 2022.
M. Finkelstein et al.,
“Explainable Reinforcement Learning via Model Transforms”, in Proceedings of the Conference on Neural Information Processing Systems (NeurIPS) , 2022.
G. Brero, N. Lepore, E. Mibuari, and D. C. Parkes,
“Learning to Mitigate AI Collusion on Economic Platforms.”, in NeurIPS 2022 , 2022.
G. Brero, N. Lepore, E. Mibuari, and D. C. Parkes,
“Learning to Mitigate AI Collusion on Economic Platforms.”, in NeurIPS 2022 , 2022.
X. Wang et al.,
“Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study.”, in Proc. of the International World Wide Web Conference (WWW ’23), 2023, pp. 3592–3602.
X. Wang et al.,
“Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study.”, in Proc. of the International World Wide Web Conference (WWW ’23), 2023, pp. 3592–3602.
J. Watson-Daniels, D. C. Parkes, and B. Ustun,
“Predictive Multiplicity in Probabilistic Classification”, in Proc. of Association for the Advancement of Artificial Intelligence, AAAI, 2023, pp. 10306–10314.
J. Watson-Daniels, D. C. Parkes, and B. Ustun,
“Predictive Multiplicity in Probabilistic Classification”, in Proc. of Association for the Advancement of Artificial Intelligence, AAAI, 2023, pp. 10306–10314.
Z. Fan, F. J. M. Cossío, B. Altschuler, H. Sun, X. Wang, and D. C. Parkes,
“Differential Liquidity Provision in Uniswap v3 and Implications for Contract Design.”, in Proc. of Association for Computing Machinery (ACM) International Conference on Artificial Intelligence in Finance (ICAIF) , 2022, pp. 9–17.
Z. Fan, F. J. M. Cossío, B. Altschuler, H. Sun, X. Wang, and D. C. Parkes,
“Differential Liquidity Provision in Uniswap v3 and Implications for Contract Design.”, in Proc. of Association for Computing Machinery (ACM) International Conference on Artificial Intelligence in Finance (ICAIF) , 2022, pp. 9–17.
S. S. Ravindranath, Z. Feng, S. Li, J. Ma, S. D. Kominers, and D. C. Parkes,
“Deep Learning for Two-Sided Matching.”, in 6th workshop in an interdisciplinary and international workshop series on matching under preferences, Vienna, Austria, 2022.
S. S. Ravindranath, Z. Feng, S. Li, J. Ma, S. D. Kominers, and D. C. Parkes,
“Deep Learning for Two-Sided Matching.”, in 6th workshop in an interdisciplinary and international workshop series on matching under preferences, Vienna, Austria, 2022.
M. Gerstgrasser, R. Trivedi, and D. C. Parkes,
“CrowdPlay: Crowdsourcing human demonstrations for offline learning.”, in International Conference on Learning Representations (ICLR) 2022, 2022.
M. Gerstgrasser, R. Trivedi, and D. C. Parkes,
“CrowdPlay: Crowdsourcing human demonstrations for offline learning.”, in International Conference on Learning Representations (ICLR) 2022, 2022.
S. Zheng, A. Trott, S. Srinivasa, D. C. Parkes, and R. Socher,
“The AI Economist: Taxation policy design via two-level deep multiagent reinforcement learning.”, Science Advances, vol. 8, no. 18, p. eabk2607, 2022.
S. Zheng, A. Trott, S. Srinivasa, D. C. Parkes, and R. Socher,
“The AI Economist: Taxation policy design via two-level deep multiagent reinforcement learning.”, Science Advances, vol. 8, no. 18, p. eabk2607, 2022.
I. Palacios-Huerta, D. C. Parkes, and R. Steinberg,
“Combinatorial Auctions in Practice”, Journal of Economics Literature, vol. 62 , no. 2, pp. 517–53, 2024.
I. Palacios-Huerta, D. C. Parkes, and R. Steinberg,
“Combinatorial Auctions in Practice”, Journal of Economics Literature, vol. 62 , no. 2, pp. 517–53, 2024.
M. V. X. Ferreira, D. J. Moroz, D. C. Parkes, and M. Stern,
“Dynamic posted-price mechanisms for the blockchain transaction-fee market”, in AFT 2021, 2021, pp. 86–99.
M. V. X. Ferreira, D. J. Moroz, D. C. Parkes, and M. Stern,
“Dynamic posted-price mechanisms for the blockchain transaction-fee market”, in AFT 2021, 2021, pp. 86–99.