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Nan Lu

Assistant Professor
University of Bristol
lunan.bit (at) gmail.com


Publications [ Google Scholar ]

    Books

  1. Book
    Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach
    Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu, Tomoya Sakai, Gang Niu
    Adaptive Computation and Machine Learning Series, MIT Press, 2022
    Page

    2. Book Chapters

  2. Chapter
    Rethinking Importance Weighting for Transfer Learning
    Nan Lu, Tianyi Zhang, Tongtong Fang, Takeshi Teshima, Masashi Sugiyama
    Federated and Transfer Learning, Springer, 2022

    3. Journal Articles

  3. Journal
    A One-step Approach to Covariate Shift Adaptation
    Tianyi Zhang, Ikko Yamane, Nan Lu, Masashi Sugiyama
    SN Computer Science, vol. 2, no. 319, 2021

  4. Journal
    Corruptions of Supervised Learning Problems: Typology and Mitigations
    Laura Iacovissi, Nan Lu, Robert C. Williamson
    Journal of Machine Learning Research (to appear), 2025

    5. Preprints

  5. Preprint
    From Many Imperfect to One Trusted: Imitation Learning from Heterogeneous Demonstrators with Unknown Expertise
    *Cheng Pan, *Nan Lu, Kai Arulkumaran, Josie Hughes (*Equal contribution)
    OpenReview Preprint

    6. Conference Papers

  6. ICASSP
    Learning from Ambiguous Data with Hard Labels
    Zeke Xie, Zheng He, Nan Lu, Lichen Bai, Bao Li, Shuo Yang, Mingming Sun, Ping Li
    IEEE ICASSP 2025

  7. NeurIPS
    Generalizing Importance Weighting to A Universal Solver for Distribution Shift Problems
    Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama
    NeurIPS 2023
    Spotlight

  8. ACML
    Learning from Multiple Unlabeled Datasets with Partial Risk Regularization
    Yuting Tang, Nan Lu, Tianyi Zhang, Masashi Sugiyama
    ACML 2022

  9. ICLR
    Federated Learning from Only Unlabeled Data with Class-conditional-sharing Clients
    Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama
    ICLR 2022

  10. ICML
    Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification
    *Nan Lu, *Shida Lei, Gang Niu, Issei Sato, Masashi Sugiyama (*Equal contribution)
    ICML 2021

  11. ICML
    Pointwise Binary Classification with Pairwise Confidence Comparisons
    Lei Feng, Senlin Shu, Nan Lu, Bo Han, Miao Xu, Gang Niu, Bo An, Masashi Sugiyama
    ICML 2021

  12. NeurIPS
    Rethinking Importance Weighting for Deep Learning under Distribution Shift
    *Tongtong Fang, *Nan Lu, Gang Niu, Masashi Sugiyama (*Equal contribution)
    NeurIPS 2020
    Spotlight

  13. AISTATS
    Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets
    Nan Lu, Tianyi Zhang, Gang Niu, Masashi Sugiyama
    AISTATS 2020
    Best Paper Award (ACML 2019 WSL Workshop)

  14. ACML
    A One-step Approach to Covariate Shift Adaptation
    Tianyi Zhang, Ikko Yamane, Nan Lu, Masashi Sugiyama
    ACML 2020
    Best Paper Award

  15. ICLR
    On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data
    Nan Lu, Gang Niu, Aditya Menon, Masashi Sugiyama
    ICLR 2019