Jonathan D. Chang

I am a 5th 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 / LinkedIn

Research

I am interested in machine learning, specifically imitation learning and reinforcement learning, and its intersection with generative models. I am particularly interested in studying how to leverage expert demonstrations and learned feature representations for scalable, efficient reinforcement learning. Recently, I have been invested in investigating imitation learning and reinforcement learning for Large Language Models, improving sample efficiency, reducing reward hacking, and developing improved RLHF algorithms.

	Provably Efficient RL with Preference-based Feedback via Dataset Reset Jonathan D. Chang, Wenhao Zhan, Kianté Brantley, Dipendra Misra, Jason D. Lee Wen Sun Preprint, 2024 arXiv upcoming New RLHF algorithm with guarantees leveraging the idea of dataset resets, to strictly improve upon PPO with no additional computation costs.
	TRIL: Transformers Reinforcement and Imitation Learning Algorithm Jonathan D. Chang, Kianté Brantley, Rajkumar Ramamurthy, Dipendra Misra, Wen Sun Repository, 2024 Github Developed, distributed research codebase for RL and IL algorithm development with large LLMs. Deepspeed, transformers, PEFT, and FSDP integration.
	Policy-Gradient Training of Large Language Models for Ranking Ge Gao, Jonathan D. Chang, Claire Cardie, Kianté Brantley, Thorsten Joachims FMDM@NeurIPS, 2023 arXiv RL finetuning of retrieval models with a Plackett-Luce ranking policy.
	Learning to Generate Better than Your LLM Jonathan D. Chang, Kianté Brantley, Rajkumar Ramamurthy, Dipendra Misra, Wen Sun Preprint, 2023 arXiv RLHF algorithmic framework combining two models, our LLM and a suboptimal expert LLM, to improve RLHF performance.
	Adversarial Imitation Learning via Boosting Jonathan D. Chang, Dhruv Sreenivas, Yingbing Huang, Kianté Brantley, Wen Sun ICLR 2024, 2024 OpenReview Principled off-policy imitatation learning algorithms through functional gradient boosting. Improves upon SOTA off-policy IL algorithms such as IQLearn.
	Learning Bellman Complete Representations for Offline Policy Evaluation Jonathan D. Chang, Kaiwen Wang, Nathan Kallus, Wen Sun ICML 2022 (Long Talk), 2022 code / arXiv Representation learning for Offline Policy Evaluation (OPE) guided by Bellman Completeness and coverage. BCRL achieves state-of-the-art evaluation on image based, continuous control tasks from Deepmind Control Suite.
	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 / arXiv Leveraging offline data with only partial coverage, MILO mitigates covariate shift in imitation learning.
	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.

Preprints

Learning Deep Parameterized Skills from Demonstration for Re-targetable Visuomotor Control
Jonathan D. Chang*, Nishanth Kumar, Sean Hastings, Aaron Gokaslan, Diego Romeres, Devesh Jha, Daniel Nikovski, George Konidaris, Stefanie Tellex
arXiv

We introduce an end-to-end method for targetable visuomotor skills as a goal-parameterized neual network policy, resulting in successfully learning a mapping from target pixel coordinates to a robot policy.

Service

Reviewer: NeurIPS 2021, ICML 2022, ICLR 2022, NeurIPS 2022, NeurIPS 2023, ICML 2023, ICLR 2024, NeurIPS 2024, ICML 2024

Website template is from Jon Barron's website.