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Jonathan D. Chang
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2020 – today
- 2024
- [c9]Jonathan D. Chang, Dhruv Sreenivas, Yingbing Huang, Kianté Brantley, Wen Sun:
Adversarial Imitation Learning via Boosting. ICLR 2024 - [i10]Owen Oertell, Jonathan D. Chang, Yiyi Zhang, Kianté Brantley, Wen Sun:
RL for Consistency Models: Faster Reward Guided Text-to-Image Generation. CoRR abs/2404.03673 (2024) - [i9]Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Kianté Brantley, Dipendra Misra, Jason D. Lee, Wen Sun:
Dataset Reset Policy Optimization for RLHF. CoRR abs/2404.08495 (2024) - [i8]Jonathan D. Chang, Dhruv Sreenivas, Yingbing Huang, Kianté Brantley, Wen Sun:
Adversarial Imitation Learning via Boosting. CoRR abs/2404.08513 (2024) - [i7]Zhaolin Gao, Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Gokul Swamy, Kianté Brantley, Thorsten Joachims, J. Andrew Bagnell, Jason D. Lee, Wen Sun:
REBEL: Reinforcement Learning via Regressing Relative Rewards. CoRR abs/2404.16767 (2024) - [i6]Zachary Ankner, Mansheej Paul, Brandon Cui, Jonathan D. Chang, Prithviraj Ammanabrolu:
Critique-out-Loud Reward Models. CoRR abs/2408.11791 (2024) - 2023
- [i5]Jonathan D. Chang, Kianté Brantley, Rajkumar Ramamurthy, Dipendra Misra, Wen Sun:
Learning to Generate Better Than Your LLM. CoRR abs/2306.11816 (2023) - [i4]Ge Gao, Jonathan D. Chang, Claire Cardie, Kianté Brantley, Thorsten Joachims:
Policy-Gradient Training of Language Models for Ranking. CoRR abs/2310.04407 (2023) - 2022
- [c8]Jonathan D. Chang, Kaiwen Wang, Nathan Kallus, Wen Sun:
Learning Bellman Complete Representations for Offline Policy Evaluation. ICML 2022: 2938-2971 - [i3]Jonathan D. Chang, Kaiwen Wang, Nathan Kallus, Wen Sun:
Learning Bellman Complete Representations for Offline Policy Evaluation. CoRR abs/2207.05837 (2022) - 2021
- [c7]Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun:
Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage. NeurIPS 2021: 965-979 - [c6]Rahul Kidambi, Jonathan D. Chang, Wen Sun:
MobILE: Model-Based Imitation Learning From Observation Alone. NeurIPS 2021: 28598-28611 - [i2]Rahul Kidambi, Jonathan D. Chang, Wen Sun:
Optimism is All You Need: Model-Based Imitation Learning From Observation Alone. CoRR abs/2102.10769 (2021) - [i1]Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun:
Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage. CoRR abs/2106.03207 (2021)
2010 – 2019
- 2014
- [c5]Viet-An Nguyen, Jordan L. Boyd-Graber, Philip Resnik, Jonathan D. Chang:
Learning a Concept Hierarchy from Multi-labeled Documents. NIPS 2014: 3671-3679 - 2011
- [b1]Jonathan D. Chang:
Uncovering, Understanding, and Predicting Links. Princeton University, USA, 2011 - 2010
- [j1]Antoine Bordes, Léon Bottou, Patrick Gallinari, Jonathan D. Chang, S. Alex Smith:
Erratum: SGDQN is Less Careful than Expected. J. Mach. Learn. Res. 11: 2229-2240 (2010)
2000 – 2009
- 2009
- [c4]Jonathan D. Chang, Jordan L. Boyd-Graber, David M. Blei:
Connections between the lines: augmenting social networks with text. KDD 2009: 169-178 - [c3]Jonathan D. Chang, Jordan L. Boyd-Graber, Sean Gerrish, Chong Wang, David M. Blei:
Reading Tea Leaves: How Humans Interpret Topic Models. NIPS 2009: 288-296 - [c2]Jonathan D. Chang, David M. Blei:
Relational Topic Models for Document Networks. AISTATS 2009: 81-88 - 2007
- [c1]Jonathan D. Chang, Miroslav Dudík, David M. Blei:
PU-BCD: Exponential Family Models for the Coarse- and Fine-Grained All-Words Tasks. SemEval@ACL 2007: 272-276
Coauthor Index
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last updated on 2024-09-30 00:07 CEST by the dblp team
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