Jonathan N. Lee

22 papers A* 4A 2C 1Journal 15

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Aletheia tackles FirstProof autonomously. CoRR Tony Feng, Junehyuk Jung, Sang-hyun Kim, Carlo Pagano, Sergei Gukov, Cheng-Chiang Tsai, David P. Woodruff, Adel Javanmard, Aryan Mokhtari, Dawsen Hwang, Yuri Chervonyi, Jonathan N. Lee, Garrett Bingham, Trieu H. Trinh, Vahab Mirrokni, Quoc V. Le, Thang Luong
2026	J	`jnl`	Towards Autonomous Mathematics Research. CoRR Tony Feng, Trieu H. Trinh, Garrett Bingham, Dawsen Hwang, Yuri Chervonyi, Junehyuk Jung, Joonkyung Lee, Carlo Pagano, Sang-hyun Kim, Federico Pasqualotto, Sergei Gukov, Jonathan N. Lee, Junsu Kim, Kaiying Hou, Golnaz Ghiasi, Yi Tay, Yaguang Li, Chenkai Kuang, Yuan Liu, Hanzhao Lin, Evan Zheran Liu, Nigamaa Nayakanti, Xiaomeng Yang, Heng-Tze Cheng, Demis Hassabis, Koray Kavukcuoglu, Quoc V. Le, Thang Luong
2024	J	`jnl`	EVOLvE: Evaluating and Optimizing LLMs For Exploration. CoRR Allen Nie, Yi Su, Bo Chang, Jonathan N. Lee, Ed H. Chi, Quoc V. Le, Minmin Chen
2024	J	`jnl`	Experiment Planning with Function Approximation. CoRR Aldo Pacchiano, Jonathan N. Lee, Emma Brunskill
2023	J	`jnl`	Estimating Optimal Policy Value in General Linear Contextual Bandits. CoRR Jonathan N. Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill
2023	J	`jnl`	Learning in POMDPs is Sample-Efficient with Hindsight Observability. CoRR Jonathan N. Lee, Alekh Agarwal, Christoph Dann, Tong Zhang
2023	J	`jnl`	Supervised Pretraining Can Learn In-Context Reinforcement Learning. CoRR Jonathan N. Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill
2022	A*	`conf`	Oracle Inequalities for Model Selection in Offline Reinforcement Learning. NeurIPS Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill
2022	J	`jnl`	Oracle Inequalities for Model Selection in Offline Reinforcement Learning. CoRR Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai, Emma Brunskill
2021	A*	`conf`	Design of Experiments for Stochastic Contextual Linear Bandits. NeurIPS Andrea Zanette, Kefan Dong, Jonathan N. Lee, Emma Brunskill
2021	J	`jnl`	Design of Experiments for Stochastic Contextual Linear Bandits. CoRR Andrea Zanette, Kefan Dong, Jonathan N. Lee, Emma Brunskill
2021	J	`jnl`	Dynamic regret convergence analysis and an adaptive regularization algorithm for on-policy robot imitation learning. Int. J. Robotics Res. Jonathan N. Lee, Michael Laskey, Ajay Kumar Tanwani, Anil Aswani, Ken Goldberg
2021	J	`jnl`	Model Selection in Batch Policy Optimization. CoRR Jonathan N. Lee, George Tucker, Ofir Nachum, Bo Dai
2021	A*	`conf`	Near Optimal Policy Optimization via REPS. NeurIPS Aldo Pacchiano, Jonathan N. Lee, Peter L. Bartlett, Ofir Nachum
2021	J	`jnl`	Near Optimal Policy Optimization via REPS. CoRR Aldo Pacchiano, Jonathan N. Lee, Peter L. Bartlett, Ofir Nachum
2021	A	`conf`	Online Model Selection for Reinforcement Learning with Function Approximation. AISTATS Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill
2020	A*	`conf`	Accelerated Message Passing for Entropy-Regularized MAP Inference. ICML Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
2020	J	`jnl`	Accelerated Message Passing for Entropy-Regularized MAP Inference. CoRR Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
2020	A	`conf`	Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference. AISTATS Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan
2020	J	`jnl`	Online Model Selection for Reinforcement Learning with Function Approximation. CoRR Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill
2019	J	`jnl`	Approximate Sherali-Adams Relaxations for MAP Inference via Entropy Regularization. CoRR Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan
2018	C	`conf`	A Dynamic Regret Analysis and Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning. WAFR Jonathan N. Lee, Michael Laskey, Ajay Kumar Tanwani, Anil Aswani, Ken Goldberg