Jonathan D. Chang

According to our database, Jonathan D. Chang authored at least 21 papers between 2007 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Regressing the Relative Future: Efficient Policy Optimization for Multi-turn RLHF.

Zhaolin Gao, Wenhao Zhan, Jonathan D. Chang, Gokul Swamy, Kianté Brantley, Jason D. Lee, Wen Sun

CoRR, 2024

Critique-out-Loud Reward Models.

Zachary Ankner, Mansheej Paul, Brandon Cui, Jonathan D. Chang, Prithviraj Ammanabrolu

CoRR, 2024

Dataset Reset Policy Optimization for RLHF.

Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Kianté Brantley, Dipendra Misra, Jason D. Lee, Wen Sun

CoRR, 2024

RL for Consistency Models: Faster Reward Guided Text-to-Image Generation.

Owen Oertell, Jonathan D. Chang, Yiyi Zhang, Kianté Brantley, Wen Sun

CoRR, 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

RLJ, 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

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Adversarial Imitation Learning via Boosting.

Jonathan D. Chang, Dhruv Sreenivas, Yingbing Huang, Kianté Brantley, Wen Sun

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Policy-Gradient Training of Language Models for Ranking.

Ge Gao, Jonathan D. Chang, Claire Cardie, Kianté Brantley, Thorsten Joachims

CoRR, 2023

Learning to Generate Better Than Your LLM.

Jonathan D. Chang, Kianté Brantley, Rajkumar Ramamurthy, Dipendra Misra, Wen Sun

CoRR, 2023

2022

Learning Bellman Complete Representations for Offline Policy Evaluation.

Jonathan D. Chang, Kaiwen Wang, Nathan Kallus, Wen Sun

Proceedings of the International Conference on Machine Learning, 2022

2021

Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage.

Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun

CoRR, 2021

Optimism is All You Need: Model-Based Imitation Learning From Observation Alone.

Rahul Kidambi, Jonathan D. Chang, Wen Sun

CoRR, 2021

MobILE: Model-Based Imitation Learning From Observation Alone.

Rahul Kidambi, Jonathan D. Chang, Wen Sun

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage.

Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2014

Learning a Concept Hierarchy from Multi-labeled Documents.

Viet-An Nguyen, Jordan L. Boyd-Graber, Philip Resnik, Jonathan D. Chang

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2011

Uncovering, Understanding, and Predicting Links

Jonathan D. Chang

PhD thesis, 2011

2010

Erratum: SGDQN is Less Careful than Expected.

Antoine Bordes, Léon Bottou, Patrick Gallinari, Jonathan D. Chang, S. Alex Smith

J. Mach. Learn. Res., 2010

2009

Relational Topic Models for Document Networks.

Jonathan D. Chang, David M. Blei

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Reading Tea Leaves: How Humans Interpret Topic Models.

Jonathan D. Chang, Jordan L. Boyd-Graber, Sean Gerrish, Chong Wang, David M. Blei

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Connections between the lines: augmenting social networks with text.

Jonathan D. Chang, Jordan L. Boyd-Graber, David M. Blei

Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28, 2009

2007

PU-BCD: Exponential Family Models for the Coarse- and Fine-Grained All-Words Tasks.

Jonathan D. Chang, Miroslav Dudík, David M. Blei

Proceedings of the 4th International Workshop on Semantic Evaluations, 2007