Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models. - Bradly C. Stadie; Sergey Levine; Pieter Abbeel (CoRR, 2015)

Third Person Imitation Learning. - Bradly C. Stadie; Pieter Abbeel; Ilya Sutskever (ICLR (Poster), 2017)

One-Shot Imitation Learning. - Yan Duan; Marcin Andrychowicz; Bradly C. Stadie; Jonathan Ho; Jonas Schneider 002; Ilya Sutskever; Pieter Abbeel; Wojciech Zaremba (NIPS, 2017)

Evolved Policy Gradients. - Rein Houthooft; Yuhua Chen; Phillip Isola; Bradly C. Stadie; Filip Wolski; Jonathan Ho; Pieter Abbeel (NeurIPS, 2018)

The Importance of Sampling inMeta-Reinforcement Learning. - Bradly C. Stadie; Ge Yang 003; Rein Houthooft; Xi Chen 022; Yan Duan; Yuhuai Wu; Pieter Abbeel; Ilya Sutskever (NeurIPS, 2018)

Evolved Policy Gradients. - Rein Houthooft; Richard Y. Chen; Phillip Isola; Bradly C. Stadie; Filip Wolski; Jonathan Ho; Pieter Abbeel (CoRR, 2018)

Some Considerations on Learning to Explore via Meta-Reinforcement Learning. - Bradly C. Stadie; Ge Yang 003; Rein Houthooft; Xi Chen 022; Yan Duan; Yuhuai Wu; Pieter Abbeel; Ilya Sutskever (CoRR, 2018)

Transfer Learning for Estimating Causal Effects using Neural Networks. - Sören R. Künzel; Bradly C. Stadie; Nikita Vemuri; Varsha Ramakrishnan; Jasjeet S. Sekhon; Pieter Abbeel (CoRR, 2018)

Learning as a Sampling Problem. - Bradly C. Stadie (2018)

One-Shot Pruning of Recurrent Neural Networks by Jacobian Spectrum Evaluation. - Matthew Shunshi Zhang; Bradly C. Stadie (ICLR, 2020)

Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning. - Silviu Pitis; Harris Chan; Stephen Zhao; Bradly C. Stadie; Jimmy Ba (ICML, 2020)

Learning Intrinsic Rewards as a Bi-Level Optimization Problem. - Bradly C. Stadie; Lunjun Zhang; Jimmy Ba (UAI, 2020)

World Model as a Graph: Learning Latent Landmarks for Planning. - Lunjun Zhang; Ge Yang 003; Bradly C. Stadie (ICML, 2021)

Invariance Through Inference. - Takuma Yoneda; Ge Yang 003; Matthew R. Walter; Bradly C. Stadie (CoRR, 2021)

Invariance Through Latent Alignment. - Takuma Yoneda; Ge Yang 003; Matthew R. Walter; Bradly C. Stadie (Robotics: Science and Systems, 2022)

Understanding Hindsight Goal Relabeling Requires Rethinking Divergence Minimization. - Lunjun Zhang; Bradly C. Stadie (CoRR, 2022)

Cold Diffusion on the Replay Buffer: Learning to Plan from Known Good States. - Zidan Wang; Takeru Oba; Takuma Yoneda; Rui Shen; Matthew R. Walter; Bradly C. Stadie (CoRL, 2023)

To the Noise and Back: Diffusion for Shared Autonomy. - Takuma Yoneda; Luzhe Sun; Ge Yang 003; Bradly C. Stadie; Matthew R. Walter (Robotics: Science and Systems, 2023)

Solving Robotics Problems in Zero-Shot with Vision-Language Models. - Zidan Wang; Rui Shen; Bradly C. Stadie (CoRR, 2024)

Expert of Experts Verification and Alignment (EVAL) Framework for Large Language Models Safety in Gastroenterology. - Mauro Giuffrè; Kisung You; Ziteng Pang; Simone Kresevic; Sunny Chung; Ryan Chen; Youngmin Ko; Colleen E. Chan; Theo Saarinen; Milos Ajcevic; Lory S. Crocè; Guadalupe Garcia-Tsao; et al. (npj Digit. Medicine, 2025)

Thoughts and Lessons on Using Visual Foundation Models for Manipulation. - Ryan Chen; Ziteng Pang; Bradly C. Stadie (Trans. Mach. Learn. Res., 2025)

Wonderful Team: Zero-Shot Physical Task Planning with Visual LLMs. - Zidan Wang; Rui Shen; Bradly C. Stadie (Trans. Mach. Learn. Res., 2025)

LAMP: Extracting Locally Linear Decision Surfaces from LLM World Models. - Ryan Chen; Youngmin Ko; Zeyu Zhang; Catherine Cho; Sunny Chung; Mauro Giuffrè; Dennis L. Shung; Bradly C. Stadie (CoRR, 2025)

Of Mice and Machines: A Comparison of Learning Between Real World Mice and RL Agents. - Shuo Han; German Espinosa; Junda Huang; Daniel A. Dombeck; Malcolm A. MacIver; Bradly C. Stadie (CoRR, 2025)