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Marvin Zhang
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2020 – today
- 2022
- [c10]Marvin Zhang, Sergey Levine, Chelsea Finn:
MEMO: Test Time Robustness via Adaptation and Augmentation. NeurIPS 2022 - 2021
- [c9]Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton Earnshaw, Imran S. Haque, Sara M. Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, Percy Liang:
WILDS: A Benchmark of in-the-Wild Distribution Shifts. ICML 2021: 5637-5664 - [c8]Marvin Zhang, Henrik Marklund, Nikita Dhawan, Abhishek Gupta, Sergey Levine, Chelsea Finn:
Adaptive Risk Minimization: Learning to Adapt to Domain Shift. NeurIPS 2021: 23664-23678 - [i9]Marvin Zhang, Sergey Levine, Chelsea Finn:
MEMO: Test Time Robustness via Adaptation and Augmentation. CoRR abs/2110.09506 (2021) - 2020
- [c7]Laura M. Smith, Nikita Dhawan, Marvin Zhang, Pieter Abbeel, Sergey Levine:
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos. Robotics: Science and Systems 2020 - [i8]Marvin Zhang, Henrik Marklund, Abhishek Gupta, Sergey Levine, Chelsea Finn:
Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift. CoRR abs/2007.02931 (2020) - [i7]Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, Percy Liang:
WILDS: A Benchmark of in-the-Wild Distribution Shifts. CoRR abs/2012.07421 (2020)
2010 – 2019
- 2019
- [c6]Marvin Zhang, Sharad Vikram, Laura M. Smith, Pieter Abbeel, Matthew J. Johnson, Sergey Levine:
SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning. ICML 2019: 7444-7453 - [c5]Michael Janner, Justin Fu, Marvin Zhang, Sergey Levine:
When to Trust Your Model: Model-Based Policy Optimization. NeurIPS 2019: 12498-12509 - [i6]Michael Janner, Justin Fu, Marvin Zhang, Sergey Levine:
When to Trust Your Model: Model-Based Policy Optimization. CoRR abs/1906.08253 (2019) - [i5]Laura M. Smith, Nikita Dhawan, Marvin Zhang, Pieter Abbeel, Sergey Levine:
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos. CoRR abs/1912.04443 (2019) - 2018
- [i4]Marvin Zhang, Sharad Vikram, Laura M. Smith, Pieter Abbeel, Matthew J. Johnson, Sergey Levine:
SOLAR: Deep Structured Latent Representations for Model-Based Reinforcement Learning. CoRR abs/1808.09105 (2018) - 2017
- [c4]Yevgen Chebotar, Karol Hausman, Marvin Zhang, Gaurav S. Sukhatme, Stefan Schaal, Sergey Levine:
Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning. ICML 2017: 703-711 - [c3]Marvin Zhang, Xinyang Geng, Jonathan Bruce, Ken Caluwaerts, Massimo Vespignani, Vytas SunSpiral, Pieter Abbeel, Sergey Levine:
Deep reinforcement learning for tensegrity robot locomotion. ICRA 2017: 634-641 - [i3]Yevgen Chebotar, Karol Hausman, Marvin Zhang, Gaurav S. Sukhatme, Stefan Schaal, Sergey Levine:
Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning. CoRR abs/1703.03078 (2017) - 2016
- [c2]Marvin Zhang, Zoe McCarthy, Chelsea Finn, Sergey Levine, Pieter Abbeel:
Learning deep neural network policies with continuous memory states. ICRA 2016: 520-527 - [c1]Nick Parlante, Julie Zelenski, Baker Franke, Arvind Bhusnurmath, Karen Her, Kristen Gee, Eric D. Manley, Timothy Urness, Marvin Zhang, Brian Hou, John DeNero, Josh Hug, Kevin Wayne:
Nifty Assignments. SIGCSE 2016: 588-589 - [i2]Xinyang Geng, Marvin Zhang, Jonathan Bruce, Ken Caluwaerts, Massimo Vespignani, Vytas SunSpiral, Pieter Abbeel, Sergey Levine:
Deep Reinforcement Learning for Tensegrity Robot Locomotion. CoRR abs/1609.09049 (2016) - 2015
- [i1]Marvin Zhang, Sergey Levine, Zoe McCarthy, Chelsea Finn, Pieter Abbeel:
Policy Learning with Continuous Memory States for Partially Observed Robotic Control. CoRR abs/1507.01273 (2015)
Coauthor Index
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