Robot grasping in clutter: Using a hierarchy of supervisors for learning from demonstrations. - Michael Laskey; Jonathan Lee 002; Caleb Chuck; David V. Gealy; Wesley Yu-Shu Hsieh; Florian T. Pokorny; Anca D. Dragan; Ken Goldberg (CASE, 2016)

DART: Noise Injection for Robust Imitation Learning. - Michael Laskey; Jonathan Lee 002; Roy Fox; Anca D. Dragan; Ken Goldberg (CoRL, 2017)

Malasakit 1.0: A participatory online platform for crowdsourcing disaster risk reduction strategies in the philippines. - Brandie Nonnecke; Shrestha Mohanty; Andrew Lee; Jonathan Lee 002; Sequoia Beckman; Justin Mi; Sanjay Krishnan; Rachel Edita Roxas; Nathaniel Oco; Camille Crittenden; Ken Goldberg (GHTC, 2017)

Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations. - Michael Laskey; Caleb Chuck; Jonathan Lee 002; Jeffrey Mahler; Sanjay Krishnan; Kevin G. Jamieson; Anca D. Dragan; Ken Goldberg (ICRA, 2017)

Iterative Noise Injection for Scalable Imitation Learning. - Michael Laskey; Jonathan Lee 002; Wesley Yu-Shu Hsieh; Richard Liaw; Jeffrey Mahler; Roy Fox; Ken Goldberg (CoRR, 2017)

Constraint Estimation and Derivative-Free Recovery for Robot Learning from Demonstrations. - Jonathan Lee 002; Michael Laskey; Roy Fox; Ken Goldberg (CASE, 2018)

Malasakit 2.0: A Participatory Online Platform with Feature Phone Integration and Voice Recognition for Crowdsourcing Disaster Risk Reduction Strategies in the Philippines. - Brandie Nonnecke; Shrestha Mohanty; Andrew Lee; Jonathan Lee 002; Sequoia Beckman; Justin Mi; Thanatcha Panpairoj; Jeffrey Rosario Ancheta; Hilary Martinez; Nathaniel Oco; Rachel Edita Roxas; Camille Crittenden; et al. (GHTC, 2018)

A Dynamic Regret Analysis and Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning. - Jonathan N. Lee; Michael Laskey; Ajay Kumar Tanwani; Anil Aswani; Ken Goldberg (WAFR, 2018)

Generalizing Robot Imitation Learning with Invariant Hidden Semi-Markov Models. - Ajay Kumar Tanwani; Jonathan Lee 002; Brijen Thananjeyan; Michael Laskey; Sanjay Krishnan; Roy Fox; Ken Goldberg; Sylvain Calinon (WAFR, 2018)

Derivative-Free Failure Avoidance Control for Manipulation using Learned Support Constraints. - Jonathan Lee 002; Michael Laskey; Roy Fox; Ken Goldberg (CoRR, 2018)

On-Policy Robot Imitation Learning from a Converging Supervisor. - Ashwin Balakrishna; Brijen Thananjeyan; Jonathan Lee 002; Felix Li; Arsh Zahed; Joseph E. Gonzalez; Ken Goldberg (CoRL, 2019)

Approximate Sherali-Adams Relaxations for MAP Inference via Entropy Regularization. - Jonathan N. Lee; Aldo Pacchiano; Michael I. Jordan (CoRR, 2019)

Continuous Online Learning and New Insights to Online Imitation Learning. - Jonathan Lee 002; Ching-An Cheng; Ken Goldberg; Byron Boots (CoRR, 2019)

Online Learning with Continuous Variations: Dynamic Regret and Reductions. - Ching-An Cheng; Jonathan Lee 002; Ken Goldberg; Byron Boots (AISTATS, 2020)

Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference. - Jonathan N. Lee; Aldo Pacchiano; Michael I. Jordan (AISTATS, 2020)

Accelerated Message Passing for Entropy-Regularized MAP Inference. - Jonathan N. Lee; Aldo Pacchiano; Peter L. Bartlett; Michael I. Jordan (ICML, 2020)

Online Model Selection for Reinforcement Learning with Function Approximation. - Jonathan N. Lee; Aldo Pacchiano; Vidya Muthukumar; Weihao Kong; Emma Brunskill (AISTATS, 2021)

Near Optimal Policy Optimization via REPS. - Aldo Pacchiano; Jonathan N. Lee; Peter L. Bartlett; Ofir Nachum (NeurIPS, 2021)

Design of Experiments for Stochastic Contextual Linear Bandits. - Andrea Zanette; Kefan Dong; Jonathan N. Lee; Emma Brunskill (NeurIPS, 2021)

Dynamic regret convergence analysis and an adaptive regularization algorithm for on-policy robot imitation learning. - Jonathan N. Lee; Michael Laskey; Ajay Kumar Tanwani; Anil Aswani; Ken Goldberg (Int. J. Robotics Res., 2021)

Sequential robot imitation learning from observations. - Ajay Kumar Tanwani; Andy Yan; Jonathan Lee 002; Sylvain Calinon; Ken Goldberg (Int. J. Robotics Res., 2021)

Model Selection in Batch Policy Optimization. - Jonathan Lee 002; George Tucker; Ofir Nachum; Bo Dai 001 (ICML, 2022)

Oracle Inequalities for Model Selection in Offline Reinforcement Learning. - Jonathan N. Lee; George Tucker; Ofir Nachum; Bo Dai 001; Emma Brunskill (NeurIPS, 2022)

Dueling RL: Reinforcement Learning with Trajectory Preferences. - Aadirupa Saha; Aldo Pacchiano; Jonathan Lee 002 (AISTATS, 2023)

Learning in POMDPs is Sample-Efficient with Hindsight Observability. - Jonathan Lee 002; Alekh Agarwal; Christoph Dann; Tong Zhang 001 (ICML, 2023)

Supervised Pretraining Can Learn In-Context Reinforcement Learning. - Jonathan Lee 002; Annie Xie; Aldo Pacchiano; Yash Chandak; Chelsea Finn; Ofir Nachum; Emma Brunskill (NeurIPS, 2023)

Experiment Planning with Function Approximation. - Aldo Pacchiano; Jonathan Lee 002; Emma Brunskill (NeurIPS, 2023)

Estimating Optimal Policy Value in General Linear Contextual Bandits. - Jonathan N. Lee; Weihao Kong; Aldo Pacchiano; Vidya Muthukumar; Emma Brunskill (CoRR, 2023)

EVOLvE: Evaluating and Optimizing LLMs For Exploration. - Allen Nie; Yi Su; Bo Chang 002; Jonathan N. Lee; Ed H. Chi; Quoc V. Le; Minmin Chen (CoRR, 2024)

Estimating Optimal Policy Value in Linear Contextual Bandits Beyond Gaussianity. - Jonathan Lee 002; Weihao Kong; Aldo Pacchiano; Vidya Muthukumar; Emma Brunskill (Trans. Mach. Learn. Res., 2024)

Cost-Aware Near-Optimal Policy Learning. - Joy He-Yueya; Jonathan Lee 002; Matthew Jörke; Emma Brunskill (AAAI, 2025)