Papers on Minimally Supervised Learning

Working papers:

• 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 ]

• Dimitri Meunier, Zhu Li, Arthur Gretton, Samory Kpotufe. Nonlinear Meta-Learning Can Guarantee Faster Rates.
  [ arXiv ]

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

Published papers:

• 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 ]