2022
Bibliographic References tagged with 2022
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F. Dominici, E. Langdon-Gray, and D. C. Parkes,
“Spinning Up a Data Science Initiative at Harvard”, Harvard Data Science Review, vol. 4, no. 4, 2022.
F. Dominici, E. Langdon-Gray, and D. C. Parkes,
“Spinning Up a Data Science Initiative at Harvard”, Harvard Data Science Review, vol. 4, no. 4, 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.
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.
Z. Feng, D. C. Parkes, and S. S. Ravindranath,
“Machine Learning for Matching Markets”, in Online matching theory and market design, N. Immorlica, F. Echenique, and V. Vazirani (eds), Cambridge University Press , 2022.
Z. Feng, D. C. Parkes, and S. S. Ravindranath,
“Machine Learning for Matching Markets”, in Online matching theory and market design, N. Immorlica, F. Echenique, and V. Vazirani (eds), Cambridge University Press , 2022.
H. Ma, F. Fang, and D. C. Parkes,
“Spatio-temporal pricing for ridesharing platforms”, Operations Research , vol. 70, no. 2, pp. 1025–1041, 2022.
H. Ma, F. Fang, and D. C. Parkes,
“Spatio-temporal pricing for ridesharing platforms”, Operations Research , vol. 70, no. 2, pp. 1025–1041, 2022.
R. Abebe et al.,
“Opinion Dynamics with Varying Susceptibility to Persuasion via Non-Convex Local”, ACM Transactions on Knowledge Discovery from Data, vol. 16, no. 2, pp. 33:1–33:34, 2022.
R. Abebe et al.,
“Opinion Dynamics with Varying Susceptibility to Persuasion via Non-Convex Local”, ACM Transactions on Knowledge Discovery from Data, vol. 16, no. 2, pp. 33:1–33:34, 2022.