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ORCIDMichael Chang 0003
Person information
- affiliation: University of California Berkeley, Department of Electrical Engineering and Computer Science, CA, USA
- affiliation: Massachusetts Institute of Technology, Department of Brain and Cognitive Science, Cambridge, MA, USA
Other persons with the same name
- Michael Chang — disambiguation page
- Michael Chang 0001
![[0000-0002-5290-7267]](https://dblp1.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-5290-7267")
— Western Digital Corporation, San Jose, CA, USA (and 1 more) - Michael Chang 0002 — University of Toronto, Department of Physiology, ON, Canada
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2020 – today
- 2023
[b1]Michael Chang:
Neural Software Abstractions. University of California, Berkeley, USA, 2023- [c10]Michael Chang, Alyssa L. Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang:
Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement. ICLR 2023 - [c9]Bhishma Dedhia, Michael Chang, Jake Snell, Tom Griffiths, Niraj K. Jha:
Im-Promptu: In-Context Composition from Image Prompts. NeurIPS 2023 - [i12]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 abs/2303.11373 (2023) - [i11]Bhishma Dedhia, Michael Chang, Jake C. Snell, Thomas L. Griffiths, Niraj K. Jha:
Im-Promptu: In-Context Composition from Image Prompts. CoRR abs/2305.17262 (2023) - 2022
- [c8]Michael Chang, Tom Griffiths, Sergey Levine:
Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation. NeurIPS 2022 - [i10]Michael Chang, Thomas L. Griffiths, Sergey Levine:
Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation. CoRR abs/2207.00787 (2022) - 2021
- [c7]Michael Chang, Sidhant Kaushik, Sergey Levine, Tom Griffiths:
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment. ICML 2021: 1452-1462 - [i9]Michael Chang, Sidhant Kaushik, Sergey Levine, Thomas L. Griffiths:
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment. CoRR abs/2106.14993 (2021) - [i8]Arnaud Fickinger, Natasha Jaques, Samyak Parajuli, Michael Chang, Nicholas Rhinehart, Glen Berseth, Stuart Russell, Sergey Levine:
Explore and Control with Adversarial Surprise. CoRR abs/2107.07394 (2021) - 2020
- [c6]Michael Chang, Sidhant Kaushik, S. Matthew Weinberg, Tom Griffiths, Sergey Levine:
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions. ICML 2020: 1437-1447 - [i7]Michael Chang, Sidhant Kaushik, S. Matthew Weinberg, Thomas L. Griffiths, Sergey Levine:
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions. CoRR abs/2007.02382 (2020)
2010 – 2019
- 2019
- [c5]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: 1439-1456 - [c4]Michael Chang, Abhishek Gupta, Sergey Levine, Thomas L. Griffiths:
Automatically Composing Representation Transformations as a Means for Generalization. ICLR (Poster) 2019 - [c3]Xue Bin Peng, Michael Chang, Grace Zhang, Pieter Abbeel, Sergey Levine:
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies. NeurIPS 2019: 3681-3692 - [i6]Xue Bin Peng, Michael Chang, Grace Zhang, Pieter Abbeel, Sergey Levine:
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies. CoRR abs/1905.09808 (2019) - [i5]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 abs/1910.12827 (2019) - 2018
- [c2]Sophia Sanborn, David Bourgin, Michael Chang, Tom Griffiths:
Representational efficiency outweighs action efficiency in human program induction. CogSci 2018 - [i4]Michael Chang, Abhishek Gupta, Sergey Levine, Thomas L. Griffiths:
Automatically Composing Representation Transformations as a Means for Generalization. CoRR abs/1807.04640 (2018) - [i3]Sophia Sanborn, David D. Bourgin, Michael Chang, Thomas L. Griffiths:
Representational efficiency outweighs action efficiency in human program induction. CoRR abs/1807.07134 (2018) - 2017
- [c1]Michael Chang, Tomer D. Ullman, Antonio Torralba, Joshua B. Tenenbaum:
A Compositional Object-Based Approach to Learning Physical Dynamics. ICLR (Poster) 2017 - 2016
- [i2]William F. Whitney, Michael Chang, Tejas D. Kulkarni, Joshua B. Tenenbaum:
Understanding Visual Concepts with Continuation Learning. CoRR abs/1602.06822 (2016) - [i1]Michael B. Chang, Tomer D. Ullman, Antonio Torralba, Joshua B. Tenenbaum:
A Compositional Object-Based Approach to Learning Physical Dynamics. CoRR abs/1612.00341 (2016)
Coauthor Index
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