Publications

Please find below my list of publications, organized by topic. See also my Google Scholar.

Online Learning and Bandits

• Linear Bandits with Memory: from Rotting to Rising (Preprint)
  Giulia Clerici, Pierre Laforgue, Nicolò Cesa-Bianchi  [paper, code]

• Multitask Online Learning: Listen to the Neighborhood Buzz (AISTATS 2024)
  Juliette Achddou, Nicolò Cesa-Bianchi, Pierre Laforgue  [paper]

• Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning (NeurIPS 2023)
  Pier Giuseppe Sessa, Pierre Laforgue, Nicolò Cesa-Bianchi, Andreas Krause  [paper, code]

• Multitask Online Mirror Descent (TMLR, 2022)
  Nicolò Cesa-Bianchi, Pierre Laforgue, Andrea Paudice, Massimiliano Pontil  [paper, slides]

• A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits (AISTATS 2022)
  Pierre Laforgue, Giulia Clerici, Nicolò Cesa-Bianchi, Ran Gilad-Bachrach  [paper, code]

Kernel Methods and Sketching

• Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels (AISTATS 2024)
  Tamim El Ahmad, Luc Brogat-Motte, Pierre Laforgue, Florence d'Alché-Buc  [paper]

• Fast Kernel Methods for Generic Lipschitz Losses via -Sparsified Sketches (TMLR, 2023)
  Tamim El Ahmad, Pierre Laforgue, Florence d'Alché-Buc  [paper]

• Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses (ICML 2020)
  Pierre Laforgue, Alex Lambert, Luc Brogat-Motte, Florence d'Alché-Buc  [paper, slides]

• Autoencoding any Data through Kernel Autoencoders (AISTATS 2019)
  Pierre Laforgue, Stephan Clémençon, Florence d'Alché-Buc  [paper, code, poster, slides]

Robust Learning and Median-of-Means

• Generalization Bounds in the Presence of Outliers: a Median-of-Means Study (ICML 2021)
  Pierre Laforgue, Guillaume Staerman, Stephan Clémençon  [paper, poster, slides]

• When OT meets MoM: Robust estimation of Wasserstein Distance (AISTATS 2021)
  Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d'Alché-Buc  [paper, code]

• On Medians-of-(Randomized)-Pairwise-Means (ICML 2019)
  Pierre Laforgue, Stephan Clémençon, Patrice Bertail  [paper, code, poster, slides]

Statistical Learning and Selection Bias

• Fighting Selection Bias in Statistical Learning: Application to Visual Recognition from Biased Image Databases (Journal of Nonparametric Statistics, 2023)
  Stephan Clémençon, Pierre Laforgue, Robin Vogel  [paper]

• Statistical Learning from Biased Training Samples (Electronic Journal of Statistics, 2022)
  Stephan Clémençon, Pierre Laforgue  [paper, code, slides, video]

PhD Dissertation

• Deep Kernel Representation Learning for Complex Data and Reliability Issues (2020)
  Pierre Laforgue  [manuscript, defense slides]