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2020 – today
- 2023
[c10]Ari Seff, Brian Cera, Dian Chen, Mason Ng, Aurick Zhou, Nigamaa Nayakanti, Khaled S. Refaat, Rami Al-Rfou, Benjamin Sapp:
MotionLM: Multi-Agent Motion Forecasting as Language Modeling. ICCV 2023: 8545-8556- [c9]Nigamaa Nayakanti, Rami Al-Rfou, Aurick Zhou, Kratarth Goel, Khaled S. Refaat, Benjamin Sapp:
Wayformer: Motion Forecasting via Simple & Efficient Attention Networks. ICRA 2023: 2980-2987 - [i11]Ari Seff, Brian Cera, Dian Chen, Mason Ng, Aurick Zhou, Nigamaa Nayakanti, Khaled S. Refaat, Rami Al-Rfou, Benjamin Sapp:
MotionLM: Multi-Agent Motion Forecasting as Language Modeling. CoRR abs/2309.16534 (2023) - 2022
- [i10]Nigamaa Nayakanti, Rami Al-Rfou, Aurick Zhou, Kratarth Goel, Khaled S. Refaat, Benjamin Sapp:
Wayformer: Motion Forecasting via Simple & Efficient Attention Networks. CoRR abs/2207.05844 (2022) - 2021
- [c8]Kevin Li, Abhishek Gupta, Ashwin Reddy, Vitchyr H. Pong, Aurick Zhou, Justin Yu, Sergey Levine:
MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning. ICML 2021: 6346-6356 - [c7]Aurick Zhou, Sergey Levine:
Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation. ICML 2021: 12803-12812 - [c6]Aurick Zhou, Sergey Levine:
Bayesian Adaptation for Covariate Shift. NeurIPS 2021: 914-927 - [i9]Kevin Li, Abhishek Gupta, Ashwin Reddy, Vitchyr Pong, Aurick Zhou, Justin Yu, Sergey Levine:
MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning. CoRR abs/2107.07184 (2021) - [i8]Aurick Zhou, Sergey Levine:
Training on Test Data with Bayesian Adaptation for Covariate Shift. CoRR abs/2109.12746 (2021) - 2020
- [c5]Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine:
Conservative Q-Learning for Offline Reinforcement Learning. NeurIPS 2020 - [i7]Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine:
Conservative Q-Learning for Offline Reinforcement Learning. CoRR abs/2006.04779 (2020) - [i6]Aurick Zhou, Sergey Levine:
Amortized Conditional Normalized Maximum Likelihood. CoRR abs/2011.02696 (2020)
2010 – 2019
- 2019
- [c4]Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, Deirdre Quillen:
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables. ICML 2019: 5331-5340 - [c3]Tuomas Haarnoja, Sehoon Ha, Aurick Zhou, Jie Tan, George Tucker, Sergey Levine:
Learning to Walk Via Deep Reinforcement Learning. Robotics: Science and Systems 2019 - [i5]Kate Rakelly, Aurick Zhou, Deirdre Quillen, Chelsea Finn, Sergey Levine:
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables. CoRR abs/1903.08254 (2019) - 2018
- [c2]Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine:
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. ICML 2018: 1856-1865 - [c1]Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou, Murtaza Dalal, Pieter Abbeel, Sergey Levine:
Composable Deep Reinforcement Learning for Robotic Manipulation. ICRA 2018: 6244-6251 - [i4]Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine:
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. CoRR abs/1801.01290 (2018) - [i3]Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou, Murtaza Dalal, Pieter Abbeel, Sergey Levine:
Composable Deep Reinforcement Learning for Robotic Manipulation. CoRR abs/1803.06773 (2018) - [i2]Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, Sergey Levine:
Soft Actor-Critic Algorithms and Applications. CoRR abs/1812.05905 (2018) - [i1]Tuomas Haarnoja, Aurick Zhou, Sehoon Ha, Jie Tan, George Tucker, Sergey Levine:
Learning to Walk via Deep Reinforcement Learning. CoRR abs/1812.11103 (2018)
Coauthor Index
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