Understanding Visual Concepts with Continuation Learning. - William F. Whitney; Michael Chang 003; Tejas D. Kulkarni; Joshua B. Tenenbaum (CoRR, 2016)

A Compositional Object-Based Approach to Learning Physical Dynamics. - Michael Chang 003; Tomer D. Ullman; Antonio Torralba 001; Joshua B. Tenenbaum (ICLR (Poster), 2017)

Representational efficiency outweighs action efficiency in human program induction. - Sophia Sanborn; David Bourgin; Michael Chang 003; Tom Griffiths 001 (CogSci, 2018)

Entity Abstraction in Visual Model-Based Reinforcement Learning. - Rishi Veerapaneni; John D. Co-Reyes; Michael Chang 003; Michael Janner; Chelsea Finn; Jiajun Wu 001; Joshua B. Tenenbaum; Sergey Levine (CoRL, 2019)

Automatically Composing Representation Transformations as a Means for Generalization. - Michael Chang 003; Abhishek Gupta 004; Sergey Levine; Thomas L. Griffiths 001 (ICLR (Poster), 2019)

MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies. - Xue Bin Peng; Michael Chang 003; Grace Zhang; Pieter Abbeel; Sergey Levine (NeurIPS, 2019)

Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions. - Michael Chang 003; Sidhant Kaushik; S. Matthew Weinberg; Tom Griffiths 001; Sergey Levine (ICML, 2020)

Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment. - Michael Chang 003; Sidhant Kaushik; Sergey Levine; Tom Griffiths 001 (ICML, 2021)

Explore and Control with Adversarial Surprise. - Arnaud Fickinger; Natasha Jaques; Samyak Parajuli; Michael Chang 003; Nicholas Rhinehart; Glen Berseth; Stuart Russell 001; Sergey Levine (CoRR, 2021)

Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation. - Michael Chang 003; Tom Griffiths 001; Sergey Levine (NeurIPS, 2022)

Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. - Michael Chang 003; Alyssa L. Dayan; Franziska Meier; Thomas L. Griffiths 001; Sergey Levine; Amy Zhang 001 (ICLR, 2023)

Im-Promptu: In-Context Composition from Image Prompts. - Bhishma Dedhia; Michael Chang 003; Jake Snell; Tom Griffiths 001; Niraj K. Jha (NeurIPS, 2023)

Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. - Michael Chang 003; Alyssa L. Dayan; Franziska Meier; Thomas L. Griffiths 001; Sergey Levine; Amy Zhang 001 (CoRR, 2023)

Neural Software Abstractions - Michael Chang 003 (2023)