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Kefan Dong
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2020 – today
- 2023
- [c12]Kefan Dong, Yannis Flet-Berliac, Allen Nie, Emma Brunskill:
Model-Based Offline Reinforcement Learning with Local Misspecification. AAAI 2023: 7423-7431 - [c11]Kefan Dong, Tengyu Ma:
Toward L_∞Recovery of Nonlinear Functions: A Polynomial Sample Complexity Bound for Gaussian Random Fields. COLT 2023: 2877-2918 - [c10]Kefan Dong, Tengyu Ma:
First Steps Toward Understanding the Extrapolation of Nonlinear Models to Unseen Domains. ICLR 2023 - [c9]Kefan Dong, Tengyu Ma:
Asymptotic Instance-Optimal Algorithms for Interactive Decision Making. ICLR 2023 - [c8]Arvind V. Mahankali, Haochen Zhang, Kefan Dong, Margalit Glasgow, Tengyu Ma:
Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time. NeurIPS 2023 - [i13]Kefan Dong, Yannis Flet-Berliac, Allen Nie, Emma Brunskill:
Model-based Offline Reinforcement Learning with Local Misspecification. CoRR abs/2301.11426 (2023) - [i12]Kefan Dong, Tengyu Ma:
Toward L∞-recovery of Nonlinear Functions: A Polynomial Sample Complexity Bound for Gaussian Random Fields. CoRR abs/2305.00322 (2023) - [i11]Arvind V. Mahankali, Jeff Z. HaoChen, Kefan Dong, Margalit Glasgow, Tengyu Ma:
Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time. CoRR abs/2306.16361 (2023) - 2022
- [i10]Kefan Dong, Tengyu Ma:
Asymptotic Instance-Optimal Algorithms for Interactive Decision Making. CoRR abs/2206.02326 (2022) - [i9]Kefan Dong, Tengyu Ma:
First Steps Toward Understanding the Extrapolation of Nonlinear Models to Unseen Domains. CoRR abs/2211.11719 (2022) - 2021
- [c7]Andrea Zanette, Kefan Dong, Jonathan N. Lee, Emma Brunskill:
Design of Experiments for Stochastic Contextual Linear Bandits. NeurIPS 2021: 22720-22731 - [c6]Kefan Dong, Jiaqi Yang, Tengyu Ma:
Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature. NeurIPS 2021: 26168-26182 - [i8]Kefan Dong, Jiaqi Yang, Tengyu Ma:
Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature. CoRR abs/2102.04168 (2021) - [i7]Andrea Zanette, Kefan Dong, Jonathan N. Lee, Emma Brunskill:
Design of Experiments for Stochastic Contextual Linear Bandits. CoRR abs/2107.09912 (2021) - 2020
- [c5]Kefan Dong, Jian Peng, Yining Wang, Yuan Zhou:
Root-n-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank. COLT 2020: 1554-1557 - [c4]Yuanhao Wang, Kefan Dong, Xiaoyu Chen, Liwei Wang:
Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP. ICLR 2020 - [c3]Kefan Dong, Yingkai Li, Qin Zhang
, Yuan Zhou:
Multinomial Logit Bandit with Low Switching Cost. ICML 2020: 2607-2615 - [c2]Kefan Dong, Yuping Luo, Tianhe Yu, Chelsea Finn, Tengyu Ma:
On the Expressivity of Neural Networks for Deep Reinforcement Learning. ICML 2020: 2627-2637 - [i6]Kefan Dong, Yingkai Li, Qin Zhang, Yuan Zhou:
Multinomial Logit Bandit with Low Switching Cost. CoRR abs/2007.04876 (2020) - [i5]Yuanhao Wang, Kefan Dong:
Refined Analysis of FPL for Adversarial Markov Decision Processes. CoRR abs/2008.09251 (2020)
2010 – 2019
- 2019
- [c1]Zhizhou Ren, Kefan Dong, Yuan Zhou, Qiang Liu, Jian Peng:
Exploration via Hindsight Goal Generation. NeurIPS 2019: 13464-13474 - [i4]Kefan Dong, Yuanhao Wang, Xiaoyu Chen, Liwei Wang:
Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP. CoRR abs/1901.09311 (2019) - [i3]Zhizhou Ren, Kefan Dong, Yuan Zhou, Qiang Liu, Jian Peng:
Exploration via Hindsight Goal Generation. CoRR abs/1906.04279 (2019) - [i2]Kefan Dong, Jian Peng, Yining Wang, Yuan Zhou:
√n-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank. CoRR abs/1909.02506 (2019) - [i1]Kefan Dong, Yuping Luo, Tengyu Ma:
Bootstrapping the Expressivity with Model-based Planning. CoRR abs/1910.05927 (2019)
Coauthor Index
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last updated on 2024-08-04 19:09 CEST by the dblp team
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