Benjamin Eysenbach

According to our database, Benjamin Eysenbach authored at least 51 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference.

Benjamin Eysenbach, Vivek Myers, Ruslan Salakhutdinov, Sergey Levine

CoRR, 2024

Closing the Gap between TD Learning and Supervised Learning - A Generalisation Point of View.

Raj Ghugare, Matthieu Geist, Glen Berseth, Benjamin Eysenbach

CoRR, 2024

Bridging State and History Representations: Understanding Self-Predictive RL.

Tianwei Ni, Benjamin Eysenbach, Erfan Seyedsalehi, Michel Ma, Clement Gehring, Aditya Mahajan, Pierre-Luc Bacon

CoRR, 2024

2023

Contrastive Difference Predictive Coding.

Chongyi Zheng, Ruslan Salakhutdinov, Benjamin Eysenbach

CoRR, 2023

A Connection between One-Step Regularization and Critic Regularization in Reinforcement Learning.

Benjamin Eysenbach, Matthieu Geist, Sergey Levine, Ruslan Salakhutdinov

CoRR, 2023

Stabilizing Contrastive RL: Techniques for Offline Goal Reaching.

Chongyi Zheng, Benjamin Eysenbach, Homer Walke, Patrick Yin, Kuan Fang, Ruslan Salakhutdinov, Sergey Levine

CoRR, 2023

HIQL: Offline Goal-Conditioned RL with Latent States as Actions.

Seohong Park, Dibya Ghosh, Benjamin Eysenbach, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment.

Tianwei Ni, Michel Ma, Benjamin Eysenbach, Pierre-Luc Bacon

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Contrastive Example-Based Control.

Kyle Beltran Hatch, Benjamin Eysenbach, Rafael Rafailov, Tianhe Yu, Ruslan Salakhutdinov, Sergey Levine, Chelsea Finn

Proceedings of the Learning for Dynamics and Control Conference, 2023

A Connection between One-Step RL and Critic Regularization in Reinforcement Learning.

Benjamin Eysenbach, Matthieu Geist, Sergey Levine, Ruslan Salakhutdinov

Proceedings of the International Conference on Machine Learning, 2023

Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts.

Amrith Setlur, Don Kurian Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective.

Raj Ghugare, Homanga Bharadhwaj, Benjamin Eysenbach, Sergey Levine, Russ Salakhutdinov

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Contrastive Value Learning: Implicit Models for Simple Offline RL.

Bogdan Mazoure, Benjamin Eysenbach, Ofir Nachum, Jonathan Tompson

Proceedings of the Conference on Robot Learning, 2023

2022

Adversarial Unlearning: Reducing Confidence Along Adversarial Directions.

Amrith Setlur, Benjamin Eysenbach, Virginia Smith, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning Options via Compression.

Yiding Jiang, Evan Zheran Liu, Benjamin Eysenbach, J. Zico Kolter, Chelsea Finn

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Contrastive Learning as Goal-Conditioned Reinforcement Learning.

Benjamin Eysenbach, Tianjun Zhang, Sergey Levine, Ruslan Salakhutdinov

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Imitating Past Successes can be Very Suboptimal.

Benjamin Eysenbach, Soumith Udatha, Russ Salakhutdinov, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Mismatched No More: Joint Model-Policy Optimization for Model-Based RL.

Benjamin Eysenbach, Alexander Khazatsky, Sergey Levine, Ruslan Salakhutdinov

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs.

Tianwei Ni, Benjamin Eysenbach, Ruslan Salakhutdinov

Proceedings of the International Conference on Machine Learning, 2022

C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks.

Tianjun Zhang, Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine, Joseph E. Gonzalez

Proceedings of the Tenth International Conference on Learning Representations, 2022

The Information Geometry of Unsupervised Reinforcement Learning.

Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine

Proceedings of the Tenth International Conference on Learning Representations, 2022

Maximum Entropy RL (Provably) Solves Some Robust RL Problems.

Benjamin Eysenbach, Sergey Levine

Proceedings of the Tenth International Conference on Learning Representations, 2022

RvS: What is Essential for Offline RL via Supervised Learning?

Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov, Sergey Levine

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Recurrent Model-Free RL is a Strong Baseline for Many POMDPs.

Tianwei Ni, Benjamin Eysenbach, Ruslan Salakhutdinov

CoRR, 2021

Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills.

Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov, Jake Varley, Alex Irpan, Benjamin Eysenbach, Ryan Julian, Chelsea Finn, Sergey Levine

CoRR, 2021

RECON: Rapid Exploration for Open-World Navigation with Latent Goal Models.

Dhruv Shah, Benjamin Eysenbach, Nicholas Rhinehart, Sergey Levine

CoRR, 2021

Robust Predictable Control.

Ben Eysenbach, Ruslan Salakhutdinov, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification.

Ben Eysenbach, Sergey Levine, Ruslan Salakhutdinov

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

ViNG: Learning Open-World Navigation with Visual Goals.

Dhruv Shah, Benjamin Eysenbach, Gregory Kahn, Nicholas Rhinehart, Sergey Levine

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills.

Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov, Jacob Varley, Alex Irpan, Benjamin Eysenbach, Ryan Julian, Chelsea Finn, Sergey Levine

Proceedings of the 38th International Conference on Machine Learning, 2021

Model-Based Visual Planning with Self-Supervised Functional Distances.

Stephen Tian, Suraj Nair, Frederik Ebert, Sudeep Dasari, Benjamin Eysenbach, Chelsea Finn, Sergey Levine

Proceedings of the 9th International Conference on Learning Representations, 2021

Learning to Reach Goals via Iterated Supervised Learning.

Dibya Ghosh, Abhishek Gupta, Ashwin Reddy, Justin Fu, Coline Manon Devin, Benjamin Eysenbach, Sergey Levine

Proceedings of the 9th International Conference on Learning Representations, 2021

C-Learning: Learning to Achieve Goals via Recursive Classification.

Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine

Proceedings of the 9th International Conference on Learning Representations, 2021

Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers.

Benjamin Eysenbach, Shreyas Chaudhari, Swapnil Asawa, Sergey Levine, Ruslan Salakhutdinov

Proceedings of the 9th International Conference on Learning Representations, 2021

Rapid Exploration for Open-World Navigation with Latent Goal Models.

Dhruv Shah, Benjamin Eysenbach, Nicholas Rhinehart, Sergey Levine

Proceedings of the Conference on Robot Learning, 8-11 November 2021, London, UK., 2021

2020

Learning to be Safe: Deep RL with a Safety Critic.

Krishnan Srinivasan, Benjamin Eysenbach, Sehoon Ha, Jie Tan, Chelsea Finn

CoRR, 2020

Interactive Visualization for Debugging RL.

Shuby Deshpande, Benjamin Eysenbach, Jeff Schneider

CoRR, 2020

Weakly-Supervised Reinforcement Learning for Controllable Behavior.

Lisa Lee, Ben Eysenbach, Ruslan Salakhutdinov, Shixiang Shane Gu, Chelsea Finn

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement.

Ben Eysenbach, Xinyang Geng, Sergey Levine, Ruslan Salakhutdinov

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

f-IRL: Inverse Reinforcement Learning via State Marginal Matching.

Tianwei Ni, Harshit S. Sikchi, Yufei Wang, Tejus Gupta, Lisa Lee, Ben Eysenbach

Proceedings of the 4th Conference on Robot Learning, 2020

2019

Learning To Reach Goals Without Reinforcement Learning.

Dibya Ghosh, Abhishek Gupta, Justin Fu, Ashwin Reddy, Coline Devin, Benjamin Eysenbach, Sergey Levine

CoRR, 2019

If MaxEnt RL is the Answer, What is the Question?

Benjamin Eysenbach, Sergey Levine

CoRR, 2019

Efficient Exploration via State Marginal Matching.

Lisa Lee, Benjamin Eysenbach, Emilio Parisotto, Eric P. Xing, Sergey Levine, Ruslan Salakhutdinov

CoRR, 2019

Unsupervised Curricula for Visual Meta-Reinforcement Learning.

Allan Jabri, Kyle Hsu, Abhishek Gupta, Ben Eysenbach, Sergey Levine, Chelsea Finn

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Search on the Replay Buffer: Bridging Planning and Reinforcement Learning.

Ben Eysenbach, Ruslan Salakhutdinov, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Diversity is All You Need: Learning Skills without a Reward Function.

Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, Sergey Levine

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Clustervision: Visual Supervision of Unsupervised Clustering.

Bum Chul Kwon, Ben Eysenbach, Janu Verma, Kenney Ng, Christopher deFilippi, Walter F. Stewart, Adam Perer

IEEE Trans. Vis. Comput. Graph., 2018

Unsupervised Meta-Learning for Reinforcement Learning.

Abhishek Gupta, Benjamin Eysenbach, Chelsea Finn, Sergey Levine

CoRR, 2018

Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings.

John D. Co-Reyes, Yuxuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, Sergey Levine

Proceedings of the 35th International Conference on Machine Learning, 2018

Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning.

Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, Sergey Levine

Proceedings of the 6th International Conference on Learning Representations, 2018

2016

Who is Mistaken?

Benjamin Eysenbach, Carl Vondrick, Antonio Torralba

CoRR, 2016