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Gérard Biau
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2020 – today
- 2024
- [c10]Nathan Doumèche, Francis Bach, Gérard Biau, Claire Boyer:
Physics-informed machine learning as a kernel method. COLT 2024: 1399-1450 - [c9]Pierre Marion, Yu-Han Wu, Michael Eli Sander, Gérard Biau:
Implicit regularization of deep residual networks towards neural ODEs. ICLR 2024 - [i18]Nathan Doumèche, Francis Bach, Claire Boyer, Gérard Biau:
Physics-informed machine learning as a kernel method. CoRR abs/2402.07514 (2024) - [i17]Nathan Doumèche, Francis Bach, Gérard Biau, Claire Boyer:
Physics-informed kernel learning. CoRR abs/2409.13786 (2024) - [i16]Pierre Marion, Raphaël Berthier, Gérard Biau, Claire Boyer:
Attention layers provably solve single-location regression. CoRR abs/2410.01537 (2024) - 2023
- [i15]Pierre Marion, Yu-Han Wu, Michael E. Sander, Gérard Biau:
Implicit regularization of deep residual networks towards neural ODEs. CoRR abs/2309.01213 (2023) - 2022
- [c8]Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet:
SHAFF: Fast and consistent SHApley eFfect estimates via random Forests. AISTATS 2022: 5563-5582 - [i14]Arthur Stéphanovitch, Ugo Tanielian, Benoît Cadre, Nicolas Klutchnikoff, Gérard Biau:
Optimal 1-Wasserstein Distance for WGANs. CoRR abs/2201.02824 (2022) - [i13]Pierre Marion, Adeline Fermanian, Gérard Biau, Jean-Philippe Vert:
Scaling ResNets in the Large-depth Regime. CoRR abs/2206.06929 (2022) - 2021
- [j19]Gérard Biau, Maxime Sangnier, Ugo Tanielian:
Some Theoretical Insights into Wasserstein GANs. J. Mach. Learn. Res. 22: 119:1-119:45 (2021) - [c7]Ugo Tanielian, Gérard Biau:
Approximating Lipschitz continuous functions with GroupSort neural networks. AISTATS 2021: 442-450 - [c6]Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet:
Interpretable Random Forests via Rule Extraction. AISTATS 2021: 937-945 - [c5]Qiming Du, Gérard Biau, François Petit, Raphaël Porcher:
Wasserstein Random Forests and Applications in Heterogeneous Treatment Effects. AISTATS 2021: 1729-1737 - [c4]Adeline Fermanian, Pierre Marion, Jean-Philippe Vert, Gérard Biau:
Framing RNN as a kernel method: A neural ODE approach. NeurIPS 2021: 3121-3134 - [i12]Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet:
SHAFF: Fast and consistent SHApley eFfect estimates via random Forests. CoRR abs/2105.11724 (2021) - [i11]Adeline Fermanian, Pierre Marion, Jean-Philippe Vert, Gérard Biau:
Framing RNN as a kernel method: A neural ODE approach. CoRR abs/2106.01202 (2021) - 2020
- [i10]Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet:
Interpretable Random Forests via Rule Extraction. CoRR abs/2004.14841 (2020) - [i9]Gérard Biau, Maxime Sangnier, Ugo Tanielian:
Some Theoretical Insights into Wasserstein GANs. CoRR abs/2006.02682 (2020) - [i8]Qiming Du, Gérard Biau, François Petit, Raphaël Porcher:
Wasserstein Random Forests and Applications in Heterogeneous Treatment Effects. CoRR abs/2006.04709 (2020) - [i7]Ugo Tanielian, Maxime Sangnier, Gérard Biau:
Approximating Lipschitz continuous functions with GroupSort neural networks. CoRR abs/2006.05254 (2020)
2010 – 2019
- 2019
- [j18]Gérard Biau
, Benoît Cadre, Laurent Rouvìère:
Accelerated gradient boosting. Mach. Learn. 108(6): 971-992 (2019) - [i6]Clément Bénard, Gérard Biau, Sébastien Da Veiga, Erwan Scornet:
SIRUS: making random forests interpretable. CoRR abs/1908.06852 (2019) - 2018
- [i5]Gérard Biau, Benoît Cadre, Laurent Rouvìère:
Accelerated Gradient Boosting. CoRR abs/1803.02042 (2018) - [i4]Gérard Biau, Benoît Cadre, Maxime Sangnier, Ugo Tanielian:
Some Theoretical Properties of GANs. CoRR abs/1803.07819 (2018) - 2017
- [i3]Gérard Biau, Benoît Cadre:
Optimization by gradient boosting. CoRR abs/1707.05023 (2017) - 2016
- [j17]Gérard Biau, Kevin Bleakley, Benoît Cadre:
The Statistical Performance of Collaborative Inference. J. Mach. Learn. Res. 17: 62:1-62:29 (2016) - [j16]Gérard Biau, Aurélie Fischer, Benjamin Guedj
COBRA: A combined regression strategy. J. Multivar. Anal. 146: 18-28 (2016) - [i2]Gérard Biau, Erwan Scornet, Johannes Welbl:
Neural Random Forests. CoRR abs/1604.07143 (2016) - 2014
- [c3]Gérard Biau, Luc Devroye:
Cellular Tree Classifiers. ALT 2014: 8-17 - 2013
- [j15]Pierre Alquier, Gérard Biau:
Sparse single-index model. J. Mach. Learn. Res. 14(1): 243-280 (2013) - [i1]Gérard Biau, Luc Devroye:
Cellular Tree Classifiers. CoRR abs/1301.4679 (2013) - 2012
- [j14]Gérard Biau:
Analysis of a Random Forests Model. J. Mach. Learn. Res. 13: 1063-1095 (2012) - [j13]Gérard Biau, Luc Devroye, Vida Dujmovic, Adam Krzyzak:
An affine invariant k-nearest neighbor regression estimate. J. Multivar. Anal. 112: 24-34 (2012) - [j12]Gérard Biau, Aurélie Fischer:
Parameter Selection for Principal Curves. IEEE Trans. Inf. Theory 58(3): 1924-1939 (2012) - [c2]Gérard Biau, Adam Krzyzak, Luc Devroye, Vida Dujmovic:
An affine invariant k-nearest neighbor regression estimate. ISIT 2012: 1445-1447 - 2011
- [j11]Gérard Biau, Benoît Patra:
Sequential Quantile Prediction of Time Series. IEEE Trans. Inf. Theory 57(3): 1664-1674 (2011) - 2010
- [j10]Gérard Biau, Frédéric Cérou, Arnaud Guyader:
On the Rate of Convergence of the Bagged Nearest Neighbor Estimate. J. Mach. Learn. Res. 11: 687-712 (2010) - [j9]Gérard Biau, Luc Devroye:
On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification. J. Multivar. Anal. 101(10): 2499-2518 (2010) - [j8]Gérard Biau, Frédéric Cérou, Arnaud Guyader:
Rates of convergence of the functional k-nearest neighbor estimate. IEEE Trans. Inf. Theory 56(4): 2034-2040 (2010)
2000 – 2009
- 2008
- [j7]Kevin Bleakley, Marie-Paule Lefranc
Recovering probabilities for nucleotide trimming processes for T cell receptor TRA and TRG V-J junctions analyzed with IMGT tools. BMC Bioinform. 9 (2008) - [j6]Gérard Biau, Luc Devroye, Gábor Lugosi:
Consistency of Random Forests and Other Averaging Classifiers. J. Mach. Learn. Res. 9: 2015-2033 (2008) - [j5]Gérard Biau, Luc Devroye, Gábor Lugosi
On the Performance of Clustering in Hilbert Spaces. IEEE Trans. Inf. Theory 54(2): 781-790 (2008) - 2007
- [c1]Kevin Bleakley, Gérard Biau, Jean-Philippe Vert
Supervised reconstruction of biological networks with local models. ISMB/ECCB (Supplement of Bioinformatics) 2007: 57-65 - 2006
- [j4]Kevin Bleakley, Véronique Giudicelli, Yan Wu, Marie-Paule Lefranc, Gérard Biau:
IMGT Standardization for Statistical Analyses of T Cell Receptor Junctions: The TRAV-TRAJ Example. Silico Biol. 6(6): 573-588 (2006) - 2005
- [j3]Gérard Biau, Florentina Bunea, Marten H. Wegkamp
Functional classification in Hilbert spaces. IEEE Trans. Inf. Theory 51(6): 2163-2172 (2005) - [j2]Gérard Biau, László Györfi:
On the asymptotic properties of a nonparametric L1-test statistic of homogeneity. IEEE Trans. Inf. Theory 51(11): 3965-3973 (2005) - 2004
- [j1]Gérard Biau, Luc Devroye:
A note on density model size testing. IEEE Trans. Inf. Theory 50(3): 576-581 (2004)
Coauthor Index
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