Chang, Michael

25 publications

ICLR 2025 INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge Angelika Romanou, Negar Foroutan, Anna Sotnikova, Sree Harsha Nelaturu, Shivalika Singh, Rishabh Maheshwary, Micol Altomare, Zeming Chen, Mohamed A. Haggag, Snegha A, Alfonso Amayuelas, Azril Hafizi Amirudin, Danylo Boiko, Michael Chang, Jenny Chim, Gal Cohen, Aditya Kumar Dalmia, Abraham Diress, Sharad Duwal, Daniil Dzenhaliou, Daniel Fernando Erazo Florez, Fabian Farestam, Joseph Marvin Imperial, Shayekh Bin Islam, Perttu Isotalo, Maral Jabbarishiviari, Börje F. Karlsson, Eldar Khalilov, Christopher Klamm, Fajri Koto, Dominik Krzemiński, Gabriel Adriano de Melo, Syrielle Montariol, Yiyang Nan, Joel Niklaus, Jekaterina Novikova, Johan Samir Obando Ceron, Debjit Paul, Esther Ploeger, Jebish Purbey, Swati Rajwal, Selvan Sunitha Ravi, Sara Rydell, Roshan Santhosh, Drishti Sharma, Marjana Prifti Skenduli, Arshia Soltani Moakhar, Bardia soltani Moakhar, Ayush Kumar Tarun, Azmine Toushik Wasi, Thenuka Ovin Weerasinghe, Serhan Yilmaz, Mike Zhang, Imanol Schlag, Marzieh Fadaee, Sara Hooker, Antoine Bosselut

ICLR 2023 Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement Michael Chang, Alyssa Li Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang

ICML 2023 Human-Timescale Adaptation in an Open-Ended Task Space Jakob Bauer, Kate Baumli, Feryal Behbahani, Avishkar Bhoopchand, Nathalie Bradley-Schmieg, Michael Chang, Natalie Clay, Adrian Collister, Vibhavari Dasagi, Lucy Gonzalez, Karol Gregor, Edward Hughes, Sheleem Kashem, Maria Loks-Thompson, Hannah Openshaw, Jack Parker-Holder, Shreya Pathak, Nicolas Perez-Nieves, Nemanja Rakicevic, Tim Rocktäschel, Yannick Schroecker, Satinder Singh, Jakub Sygnowski, Karl Tuyls, Sarah York, Alexander Zacherl, Lei M Zhang

NeurIPS 2023 Im-Promptu: In-Context Composition from Image Prompts Bhishma Dedhia, Michael Chang, Jake Snell, Tom Griffiths, Niraj Jha

NeurIPSW 2022 Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement Michael Chang, Alyssa Li Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang

NeurIPSW 2022 Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement Michael Chang, Alyssa Li Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang

NeurIPSW 2022 Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement Michael Chang, Alyssa Li Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang

NeurIPSW 2022 Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement Michael Chang, Alyssa Li Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang

NeurIPSW 2022 Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement Michael Chang, Alyssa Li Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang

NeurIPSW 2022 Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement Michael Chang, Alyssa Li Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang

NeurIPSW 2022 Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement Michael Chang, Alyssa Li Dayan, Franziska Meier, Thomas L. Griffiths, Sergey Levine, Amy Zhang

ICLRW 2022 Object Representations as Equilibria: Training Iterative Inference Algorithms with Implicit Differentiation Michael Chang, Thomas L. Griffiths, Sergey Levine

ICLRW 2022 Object Representations as Fixed Points: Training Iterative Inference Algorithms with Implicit Differentiation Michael Chang, Thomas L. Griffiths, Sergey Levine

NeurIPS 2022 Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation Michael Chang, Tom Griffiths, Sergey Levine

ICLRW 2022 Object-Centric Learning as Nested Optimization Michael Chang, Sergey Levine, Thomas L. Griffiths

ICMLW 2021 Explore and Control with Adversarial Surprise Arnaud Fickinger, Natasha Jaques, Samyak Parajuli, Michael Chang, Nicholas Rhinehart, Glen Berseth, Stuart Russell, Sergey Levine

ICML 2021 Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment Michael Chang, Sid Kaushik, Sergey Levine, Tom Griffiths

ICLRW 2021 Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment Michael Chang, Sidhant Kaushik, Thomas L. Griffiths, Sergey Levine

ICML 2020 Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions Michael Chang, Sid Kaushik, S. Matthew Weinberg, Tom Griffiths, Sergey Levine

ICLR 2019 Automatically Composing Representation Transformations as a Means for Generalization Michael Chang, Abhishek Gupta, Sergey Levine, Thomas L. Griffiths

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

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

ICMLW 2018 Automatically Constructing Compositional and Recursive Learners Michael Chang, Abhishek Gupta, Thomas Griffiths, Sergey Levine

ICLR 2018 Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and Their Interactions Sjoerd van Steenkiste, Michael Chang, Klaus Greff, Jürgen Schmidhuber

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