Jonathan D. Chang
I am a 3rd year Ph.D. student in the Computer Science department at Cornell University, advised by Wen Sun.
Before Cornell, I recieved my bachelors in Computer Science and Applied Mathematics from Brown University in 2018, working with Stefanie Tellex and George Konidaris
in the Humans to Robots (H2R) Laboratory.
CV  / 
Email  / 
Google Scholar  / 
Github
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Research
I am interested in machine learning, specifically reinforcement learning and representation learning, and its intersection with robotics. I am particularly interested in studying how to leverage expert demonstrations and learned feature representations for scalable reinforcement learning for robotic manipulation.
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Learning Bellman Complete Representations for Offline Policy Evaluation
Jonathan D. Chang*,
Kaiwen Wang*,
Nathan Kallus,
Wen Sun
ICML 2022 (Long Talk), 2022
[pdf coming soon]
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Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage
Jonathan D. Chang*,
Masatoshi Uehara*,
Dhruv Sreenivas,
Rahul Kidambi,
Wen Sun
NeurIPS 2021, 2021
code
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arXiv
Leveraging offline data with only partial coverage, MILO mitigates covariate shift in imitation learning.
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MobILE: Model-Based Imitation Learning from Observation Alone
Rahul Kidambi,
Jonathan D. Chang,
Wen Sun
NeurIPS 2021, 2021
arXiv
We show that model-based imitation learning from observations (IfO) with strategic exploration can near-optimally solve IfO both in theory and in practice.
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