d’Alche-Buc, Florence

11 publications

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

ICML 2022 Functional Output Regression with Infimal Convolution: Exploring the Huber and $ε$-Insensitive Losses Alex Lambert, Dimitri Bouche, Zoltan Szabo, Florence D’Alché-Buc

ICML 2022 Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters Luc Brogat-Motte, Rémi Flamary, Celine Brouard, Juho Rousu, Florence D’Alché-Buc

AISTATS 2021 Nonlinear Functional Output Regression: A Dictionary Approach Dimitri Bouche, Marianne Clausel, François Roueff, Florence d’Alché-Buc

AISTATS 2021 When OT Meets MoM: Robust Estimation of Wasserstein Distance Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi, Florence d’Alché-Buc

ICML 2020 Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses Pierre Laforgue, Alex Lambert, Luc Brogat-Motte, Florence D’Alché-Buc

AISTATS 2019 Autoencoding Any Data Through Kernel Autoencoders Pierre Laforgue, Stéphan Clémençon, Florence d’Alche-Buc

ACML 2019 Functional Isolation Forest Guillaume Staerman, Pavlo Mozharovskyi, Stephan Clémençon, Florence d’Alché-Buc

AISTATS 2019 Infinite Task Learning in RKHSs Romain Brault, Alex Lambert, Zoltan Szabo, Maxime Sangnier, Florence d’Alche-Buc

ICML 2018 Structured Output Learning with Abstention: Application to Accurate Opinion Prediction Alexandre Garcia, Chloé Clavel, Slim Essid, Florence d’Alche-Buc

ACML 2017 Data Sparse Nonparametric Regression with $ε$-Insensitive Losses Maxime Sangnier, Olivier Fercoq, Florence d’Alché-Buc