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Dive into the research topics where Chi Jin is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Projects
- 4 Active
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Collaborative Research: Frameworks: hpcGPT: Enhancing Computing Center User Support with HPC-enriched Generative AI
Jin, C. (PI)
NSF - National Science Foundation
8/1/24 → 7/31/27
Project: Research project
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CAREER: Foundations of Reinforcement Learning under Partial Observability
Jin, C. (PI)
NSF - National Science Foundation
8/1/23 → 7/31/28
Project: Research project
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Foundations of multiagent reinforcement learning
Jin, C. (PI)
4/1/22 → 3/31/26
Project: Research project
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RI: Medium: Provable Reinforcement Learning with Function Approximation and Neural Networks
Jin, C. (PI)
NSF - National Science Foundation
10/1/21 → 9/30/25
Project: Research project
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Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization
Lin, T., Jin, C. & Jordan, M. I., 2025, In: Journal of Machine Learning Research. 26Research output: Contribution to journal › Article › peer-review
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ZeroSwap: Data-Driven Optimal Market Making in Decentralized Finance
Nadkarni, V., Hu, J., Rana, R., Jin, C., Kulkarni, S. & Viswanath, P., 2025, Financial Cryptography and Data Security - 28th International Conference, FC 2024, Revised Selected Papers. Clark, J. & Shi, E. (eds.). Springer Science and Business Media Deutschland GmbH, p. 209-227 19 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 14744 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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CONSISTENCY MODELS AS A RICH AND EFFICIENT POLICY CLASS FOR REINFORCEMENT LEARNING
Ding, Z. & Jin, C., 2024.Research output: Contribution to conference › Paper › peer-review
5 Scopus citations -
FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning
Li, W., Ding, Z., Karten, S. & Jin, C., 2024, In: Proceedings of Machine Learning Research. 235, p. 27653-27674 22 p.Research output: Contribution to journal › Conference article › peer-review
2 Scopus citations -
MAXIMUM LIKELIHOOD ESTIMATION IS ALL YOU NEED FOR WELL-SPECIFIED COVARIATE SHIFT
Ge, J., Tang, S., Fan, J., Ma, C. & Jin, C., 2024.Research output: Contribution to conference › Paper › peer-review
1 Scopus citations