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Publications
Please find below my list of publications, organized by topic. See also my Google Scholar.
Online Learning and Bandits
Linear Bandits with Memory (TMLR, 2024)
Giulia Clerici, Pierre Laforgue, Nicolò Cesa-Bianchi [paper, code]
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
Deep Sketched Output Kernel Regression for Structured Prediction (ECML 2024)
Tamim El Ahmad, Junjie Yang, Pierre Laforgue, Florence d'Alché-Buc [paper, code]
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 p-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
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