A note on density model size testing. - Gérard Biau; Luc Devroye (IEEE Trans. Inf. Theory, 2004)

Functional classification in Hilbert spaces. - Gérard Biau; Florentina Bunea; Marten H. Wegkamp (IEEE Trans. Inf. Theory, 2005)

On the asymptotic properties of a nonparametric L1-test statistic of homogeneity. - Gérard Biau; László Györfi (IEEE Trans. Inf. Theory, 2005)

IMGT Standardization for Statistical Analyses of T Cell Receptor Junctions: The TRAV-TRAJ Example. - Kevin Bleakley; Véronique Giudicelli; Yan Wu; Marie-Paule Lefranc; Gérard Biau (Silico Biol., 2006)

Supervised reconstruction of biological networks with local models. - Kevin Bleakley; Gérard Biau; Jean-Philippe Vert (ISMB/ECCB (Supplement of Bioinformatics), 2007)

Recovering probabilities for nucleotide trimming processes for T cell receptor TRA and TRG V-J junctions analyzed with IMGT tools. - Kevin Bleakley; Marie-Paule Lefranc; Gérard Biau (BMC Bioinform., 2008)

Consistency of Random Forests and Other Averaging Classifiers. - Gérard Biau; Luc Devroye; Gábor Lugosi (J. Mach. Learn. Res., 2008)

On the Performance of Clustering in Hilbert Spaces. - Gérard Biau; Luc Devroye; Gábor Lugosi (IEEE Trans. Inf. Theory, 2008)

On the Rate of Convergence of the Bagged Nearest Neighbor Estimate. - Gérard Biau; Frédéric Cérou; Arnaud Guyader (J. Mach. Learn. Res., 2010)

On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification. - Gérard Biau; Luc Devroye (J. Multivar. Anal., 2010)

Rates of convergence of the functional k-nearest neighbor estimate. - Gérard Biau; Frédéric Cérou; Arnaud Guyader (IEEE Trans. Inf. Theory, 2010)

Sequential Quantile Prediction of Time Series. - Gérard Biau; Benoît Patra (IEEE Trans. Inf. Theory, 2011)

Analysis of a Random Forests Model. - Gérard Biau (J. Mach. Learn. Res., 2012)

An affine invariant k-nearest neighbor regression estimate. - Gérard Biau; Luc Devroye; Vida Dujmovic; Adam Krzyzak (J. Multivar. Anal., 2012)

Parameter Selection for Principal Curves. - Gérard Biau; Aurélie Fischer (IEEE Trans. Inf. Theory, 2012)

Sparse single-index model. - Pierre Alquier; Gérard Biau (J. Mach. Learn. Res., 2013)

Cellular Tree Classifiers. - Gérard Biau; Luc Devroye (ALT, 2014)

Neural Random Forests. - Gérard Biau; Erwan Scornet; Johannes Welbl (CoRR, 2016)

The Statistical Performance of Collaborative Inference. - Gérard Biau; Kevin Bleakley; Benoît Cadre (J. Mach. Learn. Res., 2016)

COBRA: A combined regression strategy. - Gérard Biau; Aurélie Fischer; Benjamin Guedj; James D. Malley (J. Multivar. Anal., 2016)

Optimization by gradient boosting. - Gérard Biau; Benoît Cadre (CoRR, 2017)

Some Theoretical Properties of GANs. - Gérard Biau; Benoît Cadre; Maxime Sangnier; Ugo Tanielian (CoRR, 2018)

SIRUS: making random forests interpretable. - Clément Bénard; Gérard Biau; Sébastien Da Veiga; Erwan Scornet (CoRR, 2019)

Accelerated gradient boosting. - Gérard Biau; Benoît Cadre; Laurent Rouvìère (Mach. Learn., 2019)

Approximating Lipschitz continuous functions with GroupSort neural networks. - Ugo Tanielian; Maxime Sangnier; Gérard Biau (CoRR, 2020)

Interpretable Random Forests via Rule Extraction. - Clément Bénard; Gérard Biau; Sébastien Da Veiga; Erwan Scornet (AISTATS, 2021)

Wasserstein Random Forests and Applications in Heterogeneous Treatment Effects. - Qiming Du; Gérard Biau; François Petit; Raphaël Porcher (AISTATS, 2021)

Approximating Lipschitz continuous functions with GroupSort neural networks. - Ugo Tanielian; Gérard Biau (AISTATS, 2021)

Framing RNN as a kernel method: A neural ODE approach. - Adeline Fermanian; Pierre Marion; Jean-Philippe Vert; Gérard Biau (NeurIPS, 2021)

Some Theoretical Insights into Wasserstein GANs. - Gérard Biau; Maxime Sangnier; Ugo Tanielian (J. Mach. Learn. Res., 2021)

SHAFF: Fast and consistent SHApley eFfect estimates via random Forests. - Clément Bénard; Gérard Biau; Sébastien Da Veiga; Erwan Scornet (AISTATS, 2022)

Optimal 1-Wasserstein Distance for WGANs. - Arthur Stéphanovitch; Ugo Tanielian; Benoît Cadre; Nicolas Klutchnikoff; Gérard Biau (CoRR, 2022)

Scaling ResNets in the Large-depth Regime. - Pierre Marion; Adeline Fermanian; Gérard Biau; Jean-Philippe Vert (CoRR, 2022)

Physics-informed machine learning as a kernel method. - Nathan Doumèche; Francis R. Bach; Gérard Biau; Claire Boyer (COLT, 2024)

Implicit regularization of deep residual networks towards neural ODEs. - Pierre Marion; Yu-Han Wu; Michael Eli Sander; Gérard Biau (ICLR, 2024)

Physics-informed kernel learning. - Nathan Doumèche; Francis R. Bach; Gérard Biau; Claire Boyer (CoRR, 2024)

Taking a Big Step: Large Learning Rates in Denoising Score Matching Prevent Memorization. - Yu-Han Wu; Pierre Marion; Gérard Biau; Claire Boyer (COLT, 2025)

Attention layers provably solve single-location regression. - Pierre Marion; Raphaël Berthier; Gérard Biau; Claire Boyer (ICLR, 2025)

Forecasting time series with constraints. - Nathan Doumèche; Francis R. Bach; Éloi Bedek; Gérard Biau; Claire Boyer; Yannig Goude (CoRR, 2025)