Construction de sous-systèmes utilisant une machine abstraite. Réalisation autour du noyau GEMAU. - Pierre Laforgue (1975)

Autoencoding any Data through Kernel Autoencoders. - Pierre Laforgue; Stéphan Clémençon; Florence d'Alché-Buc (AISTATS, 2019)

On Medians of (Randomized) Pairwise Means. - Stéphan Clémençon; Pierre Laforgue; Patrice Bertail (ICML, 2019)

Statistical Learning from Biased Training Samples. - Pierre Laforgue; Stéphan Clémençon (CoRR, 2019)

On the Dualization of Operator-Valued Kernel Machines. - Pierre Laforgue; Alex Lambert; Luc Motte; Florence d'Alché-Buc (CoRR, 2019)

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

How Robust is the Median-of-Means? Concentration Bounds in Presence of Outliers. - Pierre Laforgue; Guillaume Staerman; Stéphan Clémençon (CoRR, 2020)

When OT meets MoM: Robust estimation of Wasserstein Distance. - Guillaume Staerman; Pierre Laforgue; Pavlo Mozharovskyi; Florence d'Alché-Buc (AISTATS, 2021)

Generalization Bounds in the Presence of Outliers: a Median-of-Means Study. - Pierre Laforgue; Guillaume Staerman; Stéphan Clémençon (ICML, 2021)

Visual Recognition with Deep Learning from Biased Image Datasets. - Robin Vogel; Stéphan Clémençon; Pierre Laforgue (CoRR, 2021)

Break your Bandit Routine with LSD Rewards: a Last Switch Dependent Analysis of Satiation and Seasonality. - Pierre Laforgue; Giulia Clerici; Nicolò Cesa-Bianchi; Ran Gilad-Bachrach (CoRR, 2021)

A Last Switch Dependent Analysis of Satiation and Seasonality in Bandits. - Pierre Laforgue; Giulia Clerici; Nicolò Cesa-Bianchi; Ran Gilad-Bachrach (AISTATS, 2022)

p-Sparsified Sketches for Fast Multiple Output Kernel Methods. - Tamim El Ahmad; Pierre Laforgue; Florence d'Alché-Buc (CoRR, 2022)

Multitask Online Mirror Descent. - Nicolò Cesa-Bianchi; Pierre Laforgue; Andrea Paudice; Massimiliano Pontil (Trans. Mach. Learn. Res., 2022)

Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning. - Pier Giuseppe Sessa; Pierre Laforgue; Nicolò Cesa-Bianchi; Andreas Krause 001 (NeurIPS, 2023)

Linear Bandits with Memory: from Rotting to Rising. - Giulia Clerici; Pierre Laforgue; Nicolò Cesa-Bianchi (CoRR, 2023)

Fast Kernel Methods for Generic Lipschitz Losses via p-Sparsified Sketches. - Tamim El Ahmad; Pierre Laforgue; Florence d'Alché-Buc (Trans. Mach. Learn. Res., 2023)

Multitask Online Learning: Listen to the Neighborhood Buzz. - Juliette Achddou; Nicolò Cesa-Bianchi; Pierre Laforgue (AISTATS, 2024)

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

Deep Sketched Output Kernel Regression for Structured Prediction. - Tamim El Ahmad; Junjie Yang; Pierre Laforgue; Florence d'Alché-Buc (ECML/PKDD (3), 2024)

Linear Bandits with Memory. - Giulia Clerici; Pierre Laforgue; Nicolò Cesa-Bianchi (Trans. Mach. Learn. Res., 2024)