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Papers on Minimally Supervised Learning

Themes: active learning, semi-supervised learning, transfer learning and related distribution-shift problems

Working papers:

  • Moise Blanchard, Samory Kpotufe. Distributionally-Constrained Adversaries in Online Learning.
    [ arXiv ]

  • Yuyang Deng, Samory Kpotufe. Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning.
    [ arXiv ]

  • Steve Hanneke, Samory Kpotufe. A More Unified Theory of Transfer Learning.
    [ arXiv ]

  • Mohammadreza M. Kalan, Samory Kpotufe. Distribution-Free Rates in Neyman-Pearson Classification.
    [ arXiv ]

Published papers:

  • Mohammadreza M. Kalan, Eitan J. Neugut, Samory Kpotufe. Transfer Neyman-Pearson Algorithm for Outlier Detection.
    Artificial Intelligence and Statistics (AISTATS) 2025. [ arXiv ]

  • Dimitri Meunier, Zhu Li, Arthur Gretton, Samory Kpotufe. Nonlinear Meta-Learning Can Guarantee Faster Rates.
    SIAM Mathematics of Data Science 2025. [ arXiv ]

  • Gan Yuan, Mingyue Xu, Samory Kpotufe, Daniel Hsu. Efficient Estimation of the Central Mean Subspace via Smooth Gradient Outer Products.
    SIAM Mathematics of Data Science 2025. [ arXiv ]

  • Mohammadreza M. Kalan, Samory Kpotufe. Tight Rates in Supervised Outlier Transfer Learning.
    ICLR 2024. [ arXiv ]

  • Gan Yuan, Yunfan Zhao. Samory Kpotufe. Regimes of no gain in multi-class active learning.
    Journal of Machine Learning Research (JMLR) 2024. [ PDF ]

  • Nicholas Galbraith, Samory Kpotufe. Classification Tree Pruning Under Covariate Shift.
    IEEE Transactions on Information Theory 2024. [ arXiv ]

  • Joe Suk, Samory Kpotufe. Tracking Most Significant Switches in Nonparametric Contextual Bandits.
    Neural Information Processing Systems (NeurIPS) 2023. [ arXiv ]

  • Steve Hanneke, Samory Kpotufe, Yasaman Mahdaviyeh. Limits of Model Selection Under Transfer Learning.
    Conference on Learning Theory (COLT) 2023. [ arXiv ]

  • Joe Suk, Samory Kpotufe. Tracking Most Severe Arm Changes in Bandits.
    Conference on Learning Theory (COLT) 2022. [ arXiv ]

  • Samory Kpotufe, Gan Yuan, Yunfan Zhao. Nuances in Margin Conditions Determine Gains in Active Learning.
    Artificial Intelligence and Statistics (AISTATS) 2022. [ arXiv ]

  • Steve Hanneke, Samory Kpotufe. A No-Free-Lunch Theorem For Multitask Learning.
    Annals of Statistics 2022. [ arXiv ]

  • Samory Kpotufe, Guillaume Martinet. Marginal Singularity, and the Benefits of Labels in Covariate-Shift.
    Annals of Statistics (to appear) 2021. [ arXiv ]

  • Joe Suk, Samory Kpotufe. Self-Tuning Bandits over Unknown Covariate-Shifts.
    Algorithmic Learning Theory (ALT) 2020. [ arXiv ]

  • Steve Hanneke, Samory Kpotufe. On the Value of Target Data in Transfer Learning.
    Neural Information Processing Systems (NeurIPS) 2019. [ pdf ]

  • Samory Kpotufe, Guillaume Martinet. Marginal Singularity, and the Benefits of Labels in Covariate-Shift.
    Accepted abstract at Conference on Learning Theory (COLT) 2018. [ arXiv ]

  • Andrea Locatelli, Alexandra, Carpentier, Samory Kpotufe. An Adaptive Strategy for Active Learning with Smooth Decision Boundary.
    Algorithmic Learning Theory (ALT) 2018. [ arXiv ]

  • Andrea Locatelli, Alexandra, Carpentier, Samory Kpotufe. Adaptivity to Noise Parameters in Nonparametric Active Learning.
    Conference on Learning Theory (COLT) 2017. [ pdf ]

  • Samory Kpotufe. Lipschitz Density-Ratios, Structured Data, and Data-driven Tuning.
    Artificial Intelligence and Statistics (AISTATS) 2017. [ pdf ]

    (Also listed under Unsupervised Learning)

  • Samory Kpotufe, Ruth Urner, Shai Ben-David. Hierarchical label queries with data-dependent partitions.
    Conference on Learning Theory (COLT) 2015. [ pdf ]