PU-BCD: Exponential Family Models for the Coarse- and Fine-Grained All-Words Tasks. - Jonathan D. Chang; Miroslav Dudík; David M. Blei (SemEval@ACL, 2007)

Connections between the lines: augmenting social networks with text. - Jonathan D. Chang; Jordan L. Boyd-Graber; David M. Blei (KDD, 2009)

Reading Tea Leaves: How Humans Interpret Topic Models. - Jonathan D. Chang; Jordan L. Boyd-Graber; Sean Gerrish; Chong Wang 002; David M. Blei (NIPS, 2009)

Relational Topic Models for Document Networks. - Jonathan D. Chang; David M. Blei (AISTATS, 2009)

Erratum: SGDQN is Less Careful than Expected. - Antoine Bordes; Léon Bottou; Patrick Gallinari; Jonathan D. Chang; S. Alex Smith (J. Mach. Learn. Res., 2010)

Uncovering, Understanding, and Predicting Links - Jonathan D. Chang (2011)

Learning a Concept Hierarchy from Multi-labeled Documents. - Viet-An Nguyen; Jordan L. Boyd-Graber; Philip Resnik; Jonathan D. Chang (NIPS, 2014)

Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage. - Jonathan D. Chang; Masatoshi Uehara; Dhruv Sreenivas; Rahul Kidambi; Wen Sun 002 (NeurIPS, 2021)

MobILE: Model-Based Imitation Learning From Observation Alone. - Rahul Kidambi; Jonathan D. Chang; Wen Sun 002 (NeurIPS, 2021)

Optimism is All You Need: Model-Based Imitation Learning From Observation Alone. - Rahul Kidambi; Jonathan D. Chang; Wen Sun 002 (CoRR, 2021)

Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage. - Jonathan D. Chang; Masatoshi Uehara; Dhruv Sreenivas; Rahul Kidambi; Wen Sun 002 (CoRR, 2021)

Learning Bellman Complete Representations for Offline Policy Evaluation. - Jonathan D. Chang; Kaiwen Wang; Nathan Kallus; Wen Sun 002 (ICML, 2022)

Learning to Generate Better Than Your LLM. - Jonathan D. Chang; Kianté Brantley; Rajkumar Ramamurthy; Dipendra Misra; Wen Sun 002 (CoRR, 2023)

Policy-Gradient Training of Language Models for Ranking. - Ge Gao; Jonathan D. Chang; Claire Cardie; Kianté Brantley; Thorsten Joachims (CoRR, 2023)

Adversarial Imitation Learning via Boosting. - Jonathan D. Chang; Dhruv Sreenivas; Yingbing Huang; Kianté Brantley; Wen Sun 002 (ICLR, 2024)

REBEL: Reinforcement Learning via Regressing Relative Rewards. - Zhaolin Gao; Jonathan D. Chang; Wenhao Zhan; Owen Oertell; Gokul Swamy; Kianté Brantley; Thorsten Joachims; Drew Bagnell; Jason D. Lee; Wen Sun 002 (NeurIPS, 2024)

RL for Consistency Models: Reward Guided Text-to-Image Generation with Fast Inference. - Owen Oertell; Jonathan D. Chang; Yiyi Zhang; Kianté Brantley; Wen Sun 002 (RLJ, 2024)

RL for Consistency Models: Faster Reward Guided Text-to-Image Generation. - Owen Oertell; Jonathan D. Chang; Yiyi Zhang; Kianté Brantley; Wen Sun 002 (CoRR, 2024)

Dataset Reset Policy Optimization for RLHF. - Jonathan D. Chang; Wenhao Zhan; Owen Oertell; Kianté Brantley; Dipendra Misra; Jason D. Lee; Wen Sun 002 (CoRR, 2024)

Critique-out-Loud Reward Models. - Zachary Ankner; Mansheej Paul; Brandon Cui; Jonathan D. Chang; Prithviraj Ammanabrolu (CoRR, 2024)

Regressing the Relative Future: Efficient Policy Optimization for Multi-turn RLHF. - Zhaolin Gao; Wenhao Zhan; Jonathan Daniel Chang; Gokul Swamy; Kianté Brantley; Jason D. Lee; Wen Sun 002 (ICLR, 2025)

Q♯: Provably Optimal Distributional RL for LLM Post-Training. - Jin Peng Zhou; Kaiwen Wang; Jonathan D. Chang; Zhaolin Gao; Nathan Kallus; Kilian Q. Weinberger; Kianté Brantley; Wen Sun 002 (CoRR, 2025)

Value-Guided Search for Efficient Chain-of-Thought Reasoning. - Kaiwen Wang; Jin Peng Zhou; Jonathan D. Chang; Zhaolin Gao; Nathan Kallus; Kianté Brantley; Wen Sun 002 (CoRR, 2025)