Global Convergence and Induced Kernels of Gradient-Based Meta-Learning with Neural Nets. - Haoxiang Wang 003; Ruoyu Sun 001; Bo Li 026 (CoRR, 2020)

Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation. - Haoxiang Wang 003; Han Zhao 002; Bo Li 026 (ICML, 2021)

Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning. - Haoxiang Wang 003; Yite Wang; Ruoyu Sun 001; Bo Li 026 (CVPR, 2022)

Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond. - Haoxiang Wang 003; Bo Li 026; Han Zhao 002 (ICML, 2022)

Provable Domain Generalization via Invariant-Feature Subspace Recovery. - Haoxiang Wang 003; Haozhe Si; Bo Li 026; Han Zhao 002 (ICML, 2022)

Future gradient descent for adapting the temporal shifting data distribution in online recommendation systems. - Mao Ye 006; Ruichen Jiang; Haoxiang Wang 003; Dhruv Choudhary; Xiaocong Du; Bhargav Bhushanam; Aryan Mokhtari; Arun Kejariwal; Qiang Liu 001 (UAI, 2022)

Predicting Properties of Quantum Systems with Conditional Generative Models. - Haoxiang Wang 003; Maurice Weber; Josh A. Izaac; Cedric Yen-Yu Lin (CoRR, 2022)

Mitigating the Alignment Tax of RLHF. - Yong Lin; Hangyu Lin; Wei Xiong 015; Shizhe Diao; Jianmeng Liu; Jipeng Zhang; Rui Pan 002; Haoxiang Wang 003; Wenbin Hu 002; Hanning Zhang; Hanze Dong; Renjie Pi; et al. (CoRR, 2023)

Gradual Domain Adaptation: Theory and Algorithms. - Yifei He; Haoxiang Wang 003; Bo Li 026; Han Zhao 002 (CoRR, 2023)

Invariant-Feature Subspace Recovery: A New Class of Provable Domain Generalization Algorithms. - Haoxiang Wang 003; Gargi Balasubramaniam; Haozhe Si; Bo Li 026; Han Zhao 002 (CoRR, 2023)

Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards. - Haoxiang Wang 003; Yong Lin; Wei Xiong 015; Rui Yang 010; Shizhe Diao; Shuang Qiu; Han Zhao 002; Tong Zhang 001 (ACL (1), 2024)

SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding. - Haoxiang Wang 003; Pavan Kumar Anasosalu Vasu; Fartash Faghri; Raviteja Vemulapalli; Mehrdad Farajtabar; Sachin Mehta; Mohammad Rastegari; Oncel Tuzel; Hadi Pouransari (CVPR Workshops, 2024)

Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts. - Haoxiang Wang 003; Wei Xiong 015; Tengyang Xie; Han Zhao 002; Tong Zhang 001 (EMNLP (Findings), 2024)

Semi-Supervised Reward Modeling via Iterative Self-Training. - Yifei He; Haoxiang Wang 003; Ziyan Jiang; Alexandros Papangelis; Han Zhao 002 (EMNLP (Findings), 2024)

Mitigating the Alignment Tax of RLHF. - Yong Lin; Hangyu Lin; Wei Xiong 015; Shizhe Diao; Jianmeng Liu; Jipeng Zhang; Rui Pan 002; Haoxiang Wang 003; Wenbin Hu 002; Hanning Zhang; Hanze Dong; Renjie Pi; et al. (EMNLP, 2024)

Enhancing Compositional Generalization via Compositional Feature Alignment. - Haoxiang Wang 003; Haozhe Si; Huajie Shao; Han Zhao 002 (Trans. Mach. Learn. Res., 2024)

RLHF Workflow: From Reward Modeling to Online RLHF. - Hanze Dong; Wei Xiong 015; Bo Pang 004; Haoxiang Wang 003; Han Zhao 002; Yingbo Zhou; Nan Jiang 008; Doyen Sahoo; Caiming Xiong; Tong Zhang 001 (Trans. Mach. Learn. Res., 2024)