Aurick Zhou - RankMe

21 papers A* 9Journal 11Unranked 1

Year	Rank	Type	Title / Venue / Authors
2023	A*	`conf`	MotionLM: Multi-Agent Motion Forecasting as Language Modeling. ICCV Ari Seff, Brian Cera, Dian Chen, Mason Ng, Aurick Zhou, Nigamaa Nayakanti, Khaled S. Refaat, Rami Al-Rfou, Benjamin Sapp
2023	J	`jnl`	MotionLM: Multi-Agent Motion Forecasting as Language Modeling. CoRR Ari Seff, Brian Cera, Dian Chen, Mason Ng, Aurick Zhou, Nigamaa Nayakanti, Khaled S. Refaat, Rami Al-Rfou, Benjamin Sapp
2023	A*	`conf`	Wayformer: Motion Forecasting via Simple & Efficient Attention Networks. ICRA Nigamaa Nayakanti, Rami Al-Rfou, Aurick Zhou, Kratarth Goel, Khaled S. Refaat, Benjamin Sapp
2022	J	`jnl`	Wayformer: Motion Forecasting via Simple & Efficient Attention Networks. CoRR Nigamaa Nayakanti, Rami Al-Rfou, Aurick Zhou, Kratarth Goel, Khaled S. Refaat, Benjamin Sapp
2021	A*	`conf`	Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation. ICML Aurick Zhou, Sergey Levine
2021	A*	`conf`	Bayesian Adaptation for Covariate Shift. NeurIPS Aurick Zhou, Sergey Levine
2021	A*	`conf`	MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning. ICML Kevin Li, Abhishek Gupta, Ashwin Reddy, Vitchyr H. Pong, Aurick Zhou, Justin Yu, Sergey Levine
2021	J	`jnl`	MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning. CoRR Kevin Li, Abhishek Gupta, Ashwin Reddy, Vitchyr Pong, Aurick Zhou, Justin Yu, Sergey Levine
2021	J	`jnl`	Training on Test Data with Bayesian Adaptation for Covariate Shift. CoRR Aurick Zhou, Sergey Levine
2020	J	`jnl`	Amortized Conditional Normalized Maximum Likelihood. CoRR Aurick Zhou, Sergey Levine
2020	A*	`conf`	Conservative Q-Learning for Offline Reinforcement Learning. NeurIPS Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine
2020	J	`jnl`	Conservative Q-Learning for Offline Reinforcement Learning. CoRR Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine
2019	A*	`conf`	Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables. ICML Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, Deirdre Quillen
2019	J	`jnl`	Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables. CoRR Kate Rakelly, Aurick Zhou, Deirdre Quillen, Chelsea Finn, Sergey Levine
2019	—	`conf`	Learning to Walk Via Deep Reinforcement Learning. Robotics: Science and Systems Tuomas Haarnoja, Sehoon Ha, Aurick Zhou, Jie Tan, George Tucker, Sergey Levine
2018	A*	`conf`	Composable Deep Reinforcement Learning for Robotic Manipulation. ICRA Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou, Murtaza Dalal, Pieter Abbeel, Sergey Levine
2018	J	`jnl`	Composable Deep Reinforcement Learning for Robotic Manipulation. CoRR Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou, Murtaza Dalal, Pieter Abbeel, Sergey Levine
2018	J	`jnl`	Learning to Walk via Deep Reinforcement Learning. CoRR Tuomas Haarnoja, Aurick Zhou, Sehoon Ha, Jie Tan, George Tucker, Sergey Levine
2018	J	`jnl`	Soft Actor-Critic Algorithms and Applications. CoRR Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, Sergey Levine
2018	A*	`conf`	Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. ICML Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine
2018	J	`jnl`	Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. CoRR Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine