Aurick Zhou
Impact in
- Automotive Engineering top 10%
- Autonomous Vehicle Technology and Safety
- Artificial Intelligence top 10%
- Reinforcement Learning in Robotics
- Adversarial Robustness in Machine Learning
- Domain Adaptation and Few-Shot Learning
- Anomaly Detection Techniques and Applications
Papers in
-
- Reinforcement Learning in Robotics 4
- Adversarial Robustness in Machine Learning 2
- Anomaly Detection Techniques and Applications 2
-
- Autonomous Vehicle Technology and Safety 2
- Co-authors
- Sergey Levine (6 shared papers)Tuomas Haarnoja (2 shared papers)Benjamin Sapp (2 shared papers)Nigamaa Nayakanti (2 shared papers)Khaled S. Refaat (2 shared papers)Pieter Abbeel (1 shared paper)Rami Al‐Rfou (2 shared papers)Kratarth Goel (1 shared paper)
- Journals
- International Conference on Machine Learning (2 papers)arXiv (Cornell University) (3 papers)Neural Information Processing Systems (1 paper)
- Partner nations
- United States
In The Last Decade
Aurick Zhou
8 papers receiving 395 citations
Aurick Zhou's Hit Papers
Peers
Comparison fields: 5 of 54
- Automotive Engineering 121
- Artificial Intelligence 218
- Computer Vision and Pattern Recognition 116
- Control and Systems Engineering 96
- Building and Construction 54
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 18 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 | 139 |
| 2 | Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor | 2018 | 114 |
| 3 | 2019 | 83 | |
| 4 | 2023 | 48 | |
| 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 412 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), Adaptive Dynamic Programming Control (1 paper), Fault Detection and Control Systems (1 paper), Traffic and Road Safety (1 paper) and Fuel Cells and Related Materials (1 paper). The work is most often cited by research in Automotive Engineering (121 citations), Artificial Intelligence (218 citations), Computer Vision and Pattern Recognition (116 citations), Control and Systems Engineering (96 citations) and Building and Construction (54 citations). Aurick Zhou has collaborated with scholars based in United States. Frequent co-authors include Sergey Levine, Tuomas Haarnoja, Benjamin Sapp, Nigamaa Nayakanti, Khaled S. Refaat, Pieter Abbeel, Rami Al‐Rfou, Kratarth Goel, Chelsea Finn and Kate Rakelly. Their work appears in journals such as International Conference on Machine Learning, arXiv (Cornell University) and Neural Information Processing Systems.
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.