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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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RI: Medium: Provable Reinforcement Learning with Function Approximation and Neural Networks
NSF - National Science Foundation
10/1/21 → 9/30/24
Project: Research project
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CAREER: Foundations of Reinforcement Learning under Partial Observability
NSF - National Science Foundation
8/1/23 → 7/31/28
Project: Research project
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Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms
Jin, C., Liu, Q. & Miryoosefi, S., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, MA., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 13406-13418 13 p. (Advances in Neural Information Processing Systems; vol. 16).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
1 Scopus citations -
On Nonconvex Optimization for Machine Learning
Jin, C., Netrapalli, P., Ge, R., Kakade, S. M. & Jordan, M. I., Mar 2021, In: Journal of the ACM. 68, 2, 3418526.Research output: Contribution to journal › Article › peer-review
Open Access20 Scopus citations -
Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games
Bai, Y., Jin, C., Wang, H. & Xiong, C., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, MA., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 25799-25811 13 p. (Advances in Neural Information Processing Systems; vol. 31).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Foreword
Guo, Y., Chen, B., Jin, C. & Li, K., Jan 1 2020, Deep Reinforcement Learning: Fundamentals, Research and Applications. Springer Singapore, p. v-viResearch output: Chapter in Book/Report/Conference proceeding › Foreword/postscript
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Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition
Jin, C., Jin, T., Luo, H., Sra, S. & Yu, T., 2020, 37th International Conference on Machine Learning, ICML 2020. Daume, H. & Singh, A. (eds.). International Machine Learning Society (IMLS), p. 4810-4819 10 p. (37th International Conference on Machine Learning, ICML 2020; vol. PartF168147-7).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
9 Scopus citations