Michael Chang

Publication Activity (10 Years)

Years Active: 2016-2023
Publications (10 Years): 20

Top Topics

Credit Assignment

Iterative Refinement

Object Representations

Reinforcement Learning

Top Venues

Publications

Bhishma Dedhia, Michael Chang, Jake Snell, Tom Griffiths, Niraj K. Jha
Im-Promptu: In-Context Composition from Image Prompts. NeurIPS (2023)
Michael Chang, Alyssa L. Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang
Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. CoRR (2023)
Michael Chang, Alyssa L. Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang
Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. ICLR (2023)
Michael Chang, Tom Griffiths, Sergey Levine
Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation. NeurIPS (2022)
Michael Chang, Thomas L. Griffiths, Sergey Levine
Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation. CoRR (2022)
Arnaud Fickinger, Natasha Jaques, Samyak Parajuli, Michael Chang, Nicholas Rhinehart, Glen Berseth, Stuart Russell, Sergey Levine
Explore and Control with Adversarial Surprise. CoRR (2021)
Michael Chang, Sidhant Kaushik, Sergey Levine, Tom Griffiths
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment. ICML (2021)
Michael Chang, Sidhant Kaushik, Sergey Levine, Thomas L. Griffiths
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment. CoRR (2021)
Michael Chang, Sidhant Kaushik, S. Matthew Weinberg, Tom Griffiths, Sergey Levine
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions. ICML (2020)
Michael Chang, Sidhant Kaushik, S. Matthew Weinberg, Thomas L. Griffiths, Sergey Levine
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions. CoRR (2020)
Xue Bin Peng, Michael Chang, Grace Zhang, Pieter Abbeel, Sergey Levine
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies. CoRR (2019)
Rishi Veerapaneni, John D. Co-Reyes, Michael Chang, Michael Janner, Chelsea Finn, Jiajun Wu, Joshua B. Tenenbaum, Sergey Levine
Entity Abstraction in Visual Model-Based Reinforcement Learning. CoRR (2019)
Xue Bin Peng, Michael Chang, Grace Zhang, Pieter Abbeel, Sergey Levine
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies. NeurIPS (2019)
Rishi Veerapaneni, John D. Co-Reyes, Michael Chang, Michael Janner, Chelsea Finn, Jiajun Wu, Joshua B. Tenenbaum, Sergey Levine
Entity Abstraction in Visual Model-Based Reinforcement Learning. CoRL (2019)
Michael Chang, Abhishek Gupta, Sergey Levine, Thomas L. Griffiths
Automatically Composing Representation Transformations as a Means for Generalization. ICLR (Poster) (2019)
Sophia Sanborn, David Bourgin, Michael Chang, Tom Griffiths
Representational efficiency outweighs action efficiency in human program induction. CogSci (2018)
Michael Chang, Abhishek Gupta, Sergey Levine, Thomas L. Griffiths
Automatically Composing Representation Transformations as a Means for Generalization. CoRR (2018)
Sophia Sanborn, David D. Bourgin, Michael Chang, Thomas L. Griffiths
Representational efficiency outweighs action efficiency in human program induction. CoRR (2018)
Michael Chang, Tomer Ullman, Antonio Torralba, Joshua B. Tenenbaum
A Compositional Object-Based Approach to Learning Physical Dynamics. ICLR (Poster) (2017)
William F. Whitney, Michael Chang, Tejas D. Kulkarni, Joshua B. Tenenbaum
Understanding Visual Concepts with Continuation Learning. CoRR (2016)