Aurick Zhou

Reinforcement Learning · 2018–2025

A researcher in the Science corpus: 8 papers; trusts 299 researchers and is trusted by 788 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 212 citations into it; #95 of 2,242 by trust here, #129 by citations
• Artificial Intelligence — 204 citations into it; #514 of 35,345 by trust here, #1,335 by citations
• Computer Vision and Pattern Recognition — 62 citations into it
• Control and Systems Engineering — 54 citations into it; #7,025 of 8,257 by trust here, #3,389 by citations
• Management Science and Operations Research — 32 citations into it; #2,102 of 11,946 by trust here, #1,559 by citations
• Computational Theory and Mathematics — 24 citations into it
• Optimization & Training — 24 citations into it
• Biomedical Engineering — 22 citations into it

Whom Aurick Zhou trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Richard S. Sutton
2. Sergey Levine
3. Diederik P. Kingma
4. John Schulman
5. E. Todorov
6. Geoffrey E. Hinton
7. Pieter Abbeel
8. Ronald J. Williams
9. Yoshua Bengio
10. Alex Krizhevsky

Who trusts Aurick Zhou overall

1. Sergey Levine
2. Chelsea Finn
3. Pieter Abbeel
4. Ilya Kostrikov
5. Aviral Kumar
6. Tianhe Yu
7. Benjamin Eysenbach
8. Ofir Nachum
9. Michael Janner
10. Shixiang Shane Gu

Papers from Aurick Zhou's perspective

Ordered by how strongly the researchers Aurick Zhou trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) — 11,773 citations
• Conservative Q-Learning for Offline Reinforcement Learning (2020) — 2,669 citations
• Soft Actor-Critic Algorithms and Applications (2018) — 3,200 citations
• Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables (2019) — 832 citations
• Composable Deep Reinforcement Learning for Robotic Manipulation (2018) — 254 citations
• Learning to Walk via Deep Reinforcement Learning (2018) — 501 citations
• Wayformer: Motion Forecasting via Simple & Efficient Attention Networks (2022) — 449 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations