d'Alché-Buc, Florence

20 publications

ICLR 2025 Restyling Unsupervised Concept Based Interpretable Networks with Generative Models Jayneel Parekh, Quentin Bouniot, Pavlo Mozharovskyi, Alasdair Newson, Florence d'Alché-Buc
ICLR 2025 Tailoring Mixup to Data for Calibration Quentin Bouniot, Pavlo Mozharovskyi, Florence d'Alché-Buc
NeurIPS 2025 The Quest for the GRAph Level autoEncoder (GRALE) Paul Krzakala, Gabriel Melo, Charlotte Laclau, Florence d'Alché-Buc, Rémi Flamary
ICMLW 2024 A Framework for Differentiable Supervised Graph Prediction Paul Krzakala, Junjie Yang, Rémi Flamary, Florence d'Alché-Buc, Charlotte Laclau, Matthieu Labeau
TMLR 2024 A Pseudo-Metric Between Probability Distributions Based on Depth-Trimmed Regions Guillaume Staerman, Pavlo Mozharovskyi, Pierre Colombo, Stephan Clémençon, Florence d'Alché-Buc
NeurIPS 2024 Any2Graph: Deep End-to-End Supervised Graph Prediction with an Optimal Transport Loss Paul Krzakala, Junjie Yang, Rémi Flamary, Florence d'Alché-Buc, Charlotte Laclau, Matthieu Labeau
TMLR 2024 Exploiting Edge Features in Graph-Based Learning with Fused Network Gromov-Wasserstein Distance Junjie Yang, Matthieu Labeau, Florence d'Alché-Buc
TMLR 2023 Fast Kernel Methods for Generic Lipschitz Losses via $p$-Sparsified Sketches Tamim El Ahmad, Pierre Laforgue, Florence d'Alché-Buc
NeurIPS 2022 Listen to Interpret: Post-Hoc Interpretability for Audio Networks with NMF Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc, Gaël Richard
JMLR 2022 Vector-Valued Least-Squares Regression Under Output Regularity Assumptions Luc Brogat-Motte, Alessandro Rudi, Céline Brouard, Juho Rousu, Florence d'Alché-Buc
NeurIPS 2021 A Framework to Learn with Interpretation Jayneel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc
JMLR 2021 Improving Reproducibility in Machine Learning Research(A Report from the NeurIPS 2019 Reproducibility Program) Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Lariviere, Alina Beygelzimer, Florence d'Alche-Buc, Emily Fox, Hugo Larochelle
ICMLW 2019 A Functional Extension of Multi-Output Learning Alex Lambert, Romain Brault, Zoltan Szabo, Florence d'Alche-Buc
NeurIPS 2018 A Structured Prediction Approach for Label Ranking Anna Korba, Alexandre Garcia, Florence d'Alché-Buc
MLJ 2018 Output Fisher Embedding Regression Moussab Djerrab, Alexandre Garcia, Maxime Sangnier, Florence d'Alché-Buc
JMLR 2016 Input Output Kernel Regression: Supervised and Semi-Supervised Structured Output Prediction with Operator-Valued Kernels Céline Brouard, Marie Szafranski, Florence d'Alché-Buc
NeurIPS 2016 Joint Quantile Regression in Vector-Valued RKHSs Maxime Sangnier, Olivier Fercoq, Florence d'Alché-Buc
MLJ 2015 Operator-Valued Kernel-Based Vector Autoregressive Models for Network Inference Néhémy Lim, Florence d'Alché-Buc, Cédric Auliac, George Michailidis
NeurIPS 2003 Dynamical Modeling with Kernels for Nonlinear Time Series Prediction Liva Ralaivola, Florence d'Alché-Buc
NeurIPS 2001 Semi-Supervised MarginBoost Florence d'Alché-Buc, Yves Grandvalet, Christophe Ambroise