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ORCIDGe Yang 0003
Person information
- affiliation: Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
- affiliation: University of Chicago, Department of Physics, IL, USA
- affiliation: Facebook AI Research
Other persons with the same name
- Ge Yang — disambiguation page
- Ge Yang 0001 — Ohio State University, Department of Computer Science & Engineering, Columbus, OH, USA (and 1 more)
- Ge Yang 0002
![[0000-0001-6176-3130]](https://dblp1.uni-trier.de/img/orcid-mark.12x12.png "0000-0001-6176-3130")
— Chinese Academy of Sciences, Institute of Automation, National Laboratory of Pattern Recognition, Beijing, China (and 4 more) - Ge Yang 0004 — Nvidia Corp., Santa Clara, CA, USA (and 1 more)
- Ge Yang 0005 — Harbin Engineering of Technology, School of Computer Science and Technology, China
- Ge Yang 0006 — Peking University, Shenzhen Graduate School, China
- Ge Yang 0007 — Xidian University, School of Electronic Engineering, Xian, Shaanxi, China
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2020 – today
- 2024
[j1]Gabriel B. Margolis, Ge Yang, Kartik Paigwar, Tao Chen, Pulkit Agrawal:
Rapid locomotion via reinforcement learning. Int. J. Robotics Res. 43(4): 572-587 (2024)- 2023
- [c10]Takuma Yoneda, Luzhe Sun, Ge Yang, Bradly C. Stadie, Matthew R. Walter:
To the Noise and Back: Diffusion for Shared Autonomy. Robotics: Science and Systems 2023 - [i10]Takuma Yoneda, Luzhe Sun, Ge Yang, Bradly C. Stadie, Matthew R. Walter:
To the Noise and Back: Diffusion for Shared Autonomy. CoRR abs/2302.12244 (2023) - 2022
- [c9]Zhang-Wei Hong, Ge Yang, Pulkit Agrawal:
Bi-linear Value Networks for Multi-goal Reinforcement Learning. ICLR 2022 - [c8]Ge Yang, Anurag Ajay, Pulkit Agrawal:
Overcoming The Spectral Bias of Neural Value Approximation. ICLR 2022 - [c7]Gabriel B. Margolis, Ge Yang, Kartik Paigwar, Tao Chen, Pulkit Agrawal:
Rapid Locomotion via Reinforcement Learning. Robotics: Science and Systems 2022 - [c6]Takuma Yoneda, Ge Yang, Matthew R. Walter, Bradly C. Stadie:
Invariance Through Latent Alignment. Robotics: Science and Systems 2022 - [i9]Zhang-Wei Hong, Ge Yang, Pulkit Agrawal:
Bilinear value networks. CoRR abs/2204.13695 (2022) - [i8]Gabriel B. Margolis, Ge Yang, Kartik Paigwar, Tao Chen, Pulkit Agrawal:
Rapid Locomotion via Reinforcement Learning. CoRR abs/2205.02824 (2022) - [i7]Ge Yang, Anurag Ajay, Pulkit Agrawal:
Overcoming the Spectral Bias of Neural Value Approximation. CoRR abs/2206.04672 (2022) - 2021
- [c5]Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi S. Jaakkola:
Learning Task Informed Abstractions. ICML 2021: 3480-3491 - [c4]Lunjun Zhang, Ge Yang, Bradly C. Stadie:
World Model as a Graph: Learning Latent Landmarks for Planning. ICML 2021: 12611-12620 - [i6]Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi S. Jaakkola:
Learning Task Informed Abstractions. CoRR abs/2106.15612 (2021) - [i5]Takuma Yoneda, Ge Yang, Matthew R. Walter, Bradly C. Stadie:
Invariance Through Inference. CoRR abs/2112.08526 (2021) - 2020
- [c3]Ge Yang, Amy Zhang, Ari S. Morcos, Joelle Pineau, Pieter Abbeel, Roberto Calandra:
Plan2Vec: Unsupervised Representation Learning by Latent Plans. L4DC 2020: 935-946 - [i4]Ge Yang, Amy Zhang, Ari S. Morcos, Joelle Pineau, Pieter Abbeel, Roberto Calandra:
Plan2Vec: Unsupervised Representation Learning by Latent Plans. CoRR abs/2005.03648 (2020) - [i3]Lunjun Zhang, Ge Yang, Bradly C. Stadie:
World Model as a Graph: Learning Latent Landmarks for Planning. CoRR abs/2011.12491 (2020)
2010 – 2019
- 2018
- [c2]Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart J. Russell, Pieter Abbeel:
Learning Plannable Representations with Causal InfoGAN. NeurIPS 2018: 8747-8758 - [c1]Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever:
The Importance of Sampling inMeta-Reinforcement Learning. NeurIPS 2018: 9300-9310 - [i2]Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever:
Some Considerations on Learning to Explore via Meta-Reinforcement Learning. CoRR abs/1803.01118 (2018) - [i1]Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart Russell, Pieter Abbeel:
Learning Plannable Representations with Causal InfoGAN. CoRR abs/1807.09341 (2018)
Coauthor Index
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