Vivek Myers

I’m a PhD student at Berkeley Artificial Intelligence Research (BAIR) advised by Anca Dragan and Sergey Levine with support from an NDSEG fellowship. My research interests include reinforcement learning, human-AI interaction, and robotics. Before coming to Berkeley, I got my bachelor's degree in Computer Science and Mathematics at Stanford University, where I worked with Dorsa Sadigh.

me

Preprints

Horizon Generalization in Reinforcement Learning
Vivek Myers, Catherine Ji, Benjamin Eysenbach
2025
Accelerating Goal-Conditioned RL Algorithms and Research
Michał Bortkiewicz, Władek Pałucki, Vivek Myers, Tadeusz Dziarmaga, Tomasz Arczewski, Łukasz Kuciński, Benjamin Eysenbach
2024

Publications

Learning to Assist Humans without Inferring Rewards
Vivek Myers, Evan Ellis, Sergey Levine, Benjamin Eysenbach, Anca Dragan
Conference on Neural Information Processing Systems (NeurIPS), 2024
2024
Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference
Benjamin Eysenbach, Vivek Myers, Ruslan Salakhutdinov, Sergey Levine
Conference on Neural Information Processing Systems (NeurIPS), 2024
2024
Policy Adaptation via Language Optimization: Decomposing Tasks for Few-Shot Imitation
Vivek Myers, Bill Chunyuan Zheng, Oier Mees, Sergey Levine, Kuan Fang
Conference on Robot Learning (CoRL), 2024
2024
Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making
Vivek Myers, Chongyi Zheng, Anca Dragan, Sergey Levine, Benjamin Eysenbach
International Conference on Machine Learning (ICML), 2024
2024
Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation
Michelle Pan, Mariah Schrum, Vivek Myers, Erdem Bıyık, Anca Dragan
International Conference on Machine Learning (ICML), 2024
2024
Toward Grounded Commonsense Reasoning
Minae Kwon, Hengyuan Hu, Vivek Myers, Siddharth Karamcheti, Anca Dragan, Dorsa Sadigh
IEEE International Conference on Robotics and Automation (ICRA), 2024
2024
BridgeData V2: A Dataset for Robot Learning at Scale
Homer Walke, Kevin Black, Abraham Lee, Moo Jin Kim, Max Du, Chongyi Zheng, Tony Zhao, Philippe Hansen-Estruch, Quan Vuong, Andre He, Vivek Myers, Kuan Fang, Chelsea Finn, Sergey Levine
Conference on Robot Learning (CoRL), 2023
2023
Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control
Vivek Myers, Andre He, Kuan Fang, Homer Walke, Phillipe Hansen-Estruch, Ching-An Cheng, Mihai Jalobeanu, Andrey Kolobov, Anca Dragan, Sergey Levine
Conference on Robot Learning (CoRL), 2023
2023
Active Reward Learning from Online Preferences
Vivek Myers, Erdem Bıyık, Dorsa Sadigh
IEEE International Conference on Robotics and Automation (ICRA), 2023
2023
Learning Multimodal Rewards from Rankings
Vivek Myers, Erdem Bıyık, Nima Anari, Dorsa Sadigh
Conference on Robot Learning (CoRL), 2021 (Oral Presentation)
2021
Effective Surrogate Models for Protein Design with Bayesian Optimization
Nate Gruver, Samuel Stanton, Polina Kirichenko, Marc Finzi, Phillip Maffettone, Vivek Myers, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson
ICML 2021 Workshop on Computational Biology
2021
A Hierarchical Approach to Scaling Batch Active Search Over Structured Data
Vivek Myers, Peyton Greenside
ICML 2020 Workshop on Real World Experiment Design and Active Learning
2020
equal contribution
equal advising