Aurick Zhou

According to our database, Aurick Zhou authored at least 13 papers between 2018 and 2023.

Collaborative distances:

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

2023

Wayformer: Motion Forecasting via Simple & Efficient Attention Networks.

Nigamaa Nayakanti, Rami Al-Rfou, Aurick Zhou, Kratarth Goel, Khaled S. Refaat, Benjamin Sapp

Proceedings of the IEEE International Conference on Robotics and Automation, 2023

MotionLM: Multi-Agent Motion Forecasting as Language Modeling.

Ari Seff, Brian Cera, Dian Chen, Mason Ng, Aurick Zhou, Nigamaa Nayakanti, Khaled S. Refaat, Rami Al-Rfou, Benjamin Sapp

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2021

Training on Test Data with Bayesian Adaptation for Covariate Shift.

Aurick Zhou, Sergey Levine

CoRR, 2021

Bayesian Adaptation for Covariate Shift.

Aurick Zhou, Sergey Levine

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

Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation.

Aurick Zhou, Sergey Levine

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

MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning.

Kevin Li, Abhishek Gupta, Ashwin Reddy, Vitchyr H. Pong, Aurick Zhou, Justin Yu, Sergey Levine

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

2020

Amortized Conditional Normalized Maximum Likelihood.

Aurick Zhou, Sergey Levine

CoRR, 2020

Conservative Q-Learning for Offline Reinforcement Learning.

Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine

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

2019

Learning to Walk Via Deep Reinforcement Learning.

Tuomas Haarnoja, Sehoon Ha, Aurick Zhou, Jie Tan, George Tucker, Sergey Levine

Proceedings of the Robotics: Science and Systems XV, 2019

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables.

Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, Deirdre Quillen

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Soft Actor-Critic Algorithms and Applications.

Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, Sergey Levine

CoRR, 2018

Composable Deep Reinforcement Learning for Robotic Manipulation.

Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou, Murtaza Dalal, Pieter Abbeel, Sergey Levine

Proceedings of the 2018 IEEE International Conference on Robotics and Automation, 2018

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor.

Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine

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