Aurick Zhou
Impact in
- Automotive Engineering top 10%
- Autonomous Vehicle Technology and Safety
- Artificial Intelligence top 10%
- Reinforcement Learning in Robotics
- Anomaly Detection Techniques and Applications
- Adversarial Robustness in Machine Learning
- Domain Adaptation and Few-Shot Learning
Papers in
-
- Reinforcement Learning in Robotics 4
- Adversarial Robustness in Machine Learning 2
- Anomaly Detection Techniques and Applications 2
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- Autonomous Vehicle Technology and Safety 2
- Co-authors
- Sergey Levine (6 shared papers)Tuomas Haarnoja (2 shared papers)Pieter Abbeel (1 shared paper)Nigamaa Nayakanti (2 shared papers)Rami Al‐Rfou (2 shared papers)Khaled S. Refaat (2 shared papers)Benjamin Sapp (2 shared papers)Kratarth Goel (1 shared paper)
- Journals
- arXiv (Cornell University) (3 papers)Neural Information Processing Systems (1 paper)International Conference on Machine Learning (2 papers)
- Partner nations
- United States
In The Last Decade
Aurick Zhou
8 papers receiving 349 citations
Aurick Zhou's Hit Papers
Peers
Comparison fields: 5 of 52
- Automotive Engineering 103
- Artificial Intelligence 196
- Computer Vision and Pattern Recognition 95
- Building and Construction 52
- Control and Systems Engineering 87
Countries citing papers authored by Aurick Zhou
This map shows the geographic impact of Aurick Zhou's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Aurick Zhou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aurick Zhou more than expected).
Fields of papers citing papers by Aurick Zhou
This network shows the impact of papers produced by Aurick Zhou. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Aurick Zhou. The network helps show where Aurick Zhou may publish in the future.
Co-authors
The 17 scholars most cited alongside Aurick Zhou, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Wayformer: Motion Forecasting via Simple & Efficient Attention Networks Hit paper breakdown → | 2023 | 118 |
| 2 | Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor | 2018 | 108 |
| 3 | 2019 | 75 | |
| 4 | 2023 | 35 | |
| 5 | 2018 | 15 | |
| 6 | Bayesian Adaptation for Covariate Shift | 2021 | 6 |
| 7 | Conservative Q-Learning for Offline Reinforcement Learning | 2020 | 5 |
| 8 | Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation | 2021 | 2 |
About Aurick Zhou
Aurick Zhou is a scholar working on Artificial Intelligence, Automotive Engineering, Control and Systems Engineering, Computer Vision and Pattern Recognition and Signal Processing, having authored 8 papers that have together received 364 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (4 papers), Autonomous Vehicle Technology and Safety (2 papers), Adversarial Robustness in Machine Learning (2 papers), Anomaly Detection Techniques and Applications (2 papers), Time Series Analysis and Forecasting (1 paper), Data Visualization and Analytics (1 paper), Adaptive Dynamic Programming Control (1 paper) and Prosthetics and Rehabilitation Robotics (1 paper). The work is most often cited by research in Automotive Engineering (103 citations), Artificial Intelligence (196 citations), Computer Vision and Pattern Recognition (95 citations), Building and Construction (52 citations) and Control and Systems Engineering (87 citations). Aurick Zhou has collaborated with scholars based in United States. Frequent co-authors include Sergey Levine, Tuomas Haarnoja, Pieter Abbeel, Nigamaa Nayakanti, Rami Al‐Rfou, Khaled S. Refaat, Benjamin Sapp, Kratarth Goel, Deirdre Quillen and Chelsea Finn. Their work appears in journals such as arXiv (Cornell University), Neural Information Processing Systems and International Conference on Machine Learning.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.