( * indicates equal contribution. )

  1. Predicting Properties of Quantum Systems with Conditional Generative Models [Code]

    Haoxiang Wang, Maurice Weber, Josh Izaac, Cedric Yen-Yu Lin

    arXiv, 2022

  2. Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond [Code]

    Haoxiang Wang, Bo Li, Han Zhao

    International Conference on Machine Learning (ICML), 2022

  3. Provable Domain Generalization via Invariant-Feature Subspace Recovery [Code]

    Haoxiang Wang, Haozhe Si, Bo Li, Han Zhao

    International Conference on Machine Learning (ICML), 2022

  4. Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation System

    Mao Ye, Ruichen Jiang, Haoxiang Wang, Dhruv Choudhary, Xiaocong Du, Bhargav Bhushanam, Aryan Mokhtari, Arun Kejariwal, Qiang Liu

    Uncertainty in Artificial Intelligence (UAI), 2022

  5. Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning [Code]

    Haoxiang Wang*, Yite Wang*, Ruoyu Sun, Bo Li

    Conference on Computer Vision and Pattern Recognition (CVPR), 2022

  6. Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation [Code, Video, Slides]

    Haoxiang Wang, Han Zhao, Bo Li

    International Conference on Machine Learning (ICML), 2021

  7. Global Convergence and Generalization Bound of Gradient-Based Meta-Learning with Deep Neural Nets

    Haoxiang Wang, Ruoyu Sun, Bo Li

    arXiv, 2020

  8. Learning Positive Functions with Pseudo Mirror Descent

    Yingxiang Yang, Haoxiang Wang, Negar Kiyavash, Niao He.

    Advances in Neural Information Processing Systems (NeurIPS), 2019 (spotlight, acceptance rate: 164/6743 = 2.4%)