Ge Yang

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 13

Top Topics

Noise Sensitivity

Feature Hierarchies

Reinforcement Learning

Top Venues

Robotics: Science and Systems

Publications

Gabriel B. Margolis, Ge Yang, Kartik Paigwar, Tao Chen, Pulkit Agrawal
Rapid locomotion via reinforcement learning. Int. J. Robotics Res. 43 (4) (2024)
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)
Takuma Yoneda, Luzhe Sun, Ge Yang, Bradly C. Stadie, Matthew R. Walter
To the Noise and Back: Diffusion for Shared Autonomy. CoRR (2023)
Takuma Yoneda, Ge Yang, Matthew R. Walter, Bradly C. Stadie
Invariance Through Latent Alignment. Robotics: Science and Systems (2022)
Takuma Yoneda, Ge Yang, Matthew R. Walter, Bradly C. Stadie
Invariance Through Inference. CoRR (2021)
Lunjun Zhang, Ge Yang, Bradly C. Stadie
World Model as a Graph: Learning Latent Landmarks for Planning. ICML (2021)
Ge Yang, Amy Zhang, Ari S. Morcos, Joelle Pineau, Pieter Abbeel, Roberto Calandra
Plan2Vec: Unsupervised Representation Learning by Latent Plans. CoRR (2020)
Lunjun Zhang, Ge Yang, Bradly C. Stadie
World Model as a Graph: Learning Latent Landmarks for Planning. CoRR (2020)
Ge Yang, Amy Zhang, Ari S. Morcos, Joelle Pineau, Pieter Abbeel, Roberto Calandra
Plan2Vec: Unsupervised Representation Learning by Latent Plans. L4DC (2020)
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 (2018)
Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart J. Russell, Pieter Abbeel
Learning Plannable Representations with Causal InfoGAN. NeurIPS (2018)
Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart J. Russell, Pieter Abbeel
Learning Plannable Representations with Causal InfoGAN. CoRR (2018)
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)