Journal papers, Book sections (Published/To appear)
- I. Meah, G. Blanchard, E. Roquain.
False discovery proportion envelopes with m-consistency.
JMLR 25 (270), 1-52, 2024.
[JMLR]
- G. Blanchard, A. Carpentier, O. Zadorozhnyi.
Moment inequalities for sums of weakly dependent random fields.
Bernoulli 30(3): 2501-2520, 2024.
[arXiv]
- M. Perrot-Dockès, G. Blanchard, P. Neuvial, E. Roquain.
Post hoc false discovery proportion inference under a hidden Markov model.
TEST 32: 1365-1391, 2023.
[HAL]
- G. Blanchard, J-B. Fermanian.
Nonasymptotic one-and two-sample tests in high dimension with unknown covariance structure.
Foundations of Modern Statistics (Festschrift in honor of V. Spokoiny), D. Belomestny, C. Butucea, E. Mammen, E. Moulines editors, 121-162, Springer, 2023.
[HAL]
- G. Blanchard, P. Neuvial, E. Roquain.
On agnostic post hoc approaches to false positive control.
In Handbook of multiple comparisons, X. Pui, T. Dickhaus, Y. Ding, J. Hsu editors,
211-232, Chapman et Hall/CRC, 2022.
[arXiv]
- O. Hacquard, K. Balasubramanian, G. Blanchard, C. Levrard, W. Polonik.
Topologically penalized regression on manifolds.
Journal of machine learning research, 23(161):1-39, 2022.
[JMLR]
- T. Mary-Huard, V. Perduca, M-L. Martin-Magniette, G. Blanchard.
Error rate control for classification rules in multiclass mixture models.
The International Journal of Biostatistics, 2021.
[HAL]
- R. Gribonval, G. Blanchard, N. Keriven, Y. Traonmilin.
Compressive statistical learning with random feature moments.
Mathematical Statistics and Learning, 113-164, 2021.
[arXiv]
- R. Gribonval, G. Blanchard, N. Keriven, Y. Traonmilin.
Statistical learning guarantees for compressive clustering and compressive mixture modeling.
Mathematical Statistics and Learning, 165-257, 2021.
[arXiv]
- G. Blanchard, A. Deshmukh, U. Dogan, G. Lee, C. Scott.
Domain generalization by marginal transfer learning.
Journal of Machine Learning Research 22 (2), 1-55, 2021.
[JMLR]
- G. Blanchard, P. Neuvial, E. Roquain.
Post hoc confidence bounds on false positives using reference families.
Annals of Statistics 48 (3), 1281-1303, 2020.
[arXiv]
- G. Durand, G. Blanchard, P. Neuvial, E. Roquain.
Post hoc false positive control for structured hypotheses.
Scandinavian Journal of Statistics 47: 1114-1148, 2020.
[arXiv]
- G. Blanchard, N. Mücke
Kernel regression, minimax rates and effective dimensionality: beyond the regular case.
Analysis and Applications 18 (4): 683-693, 2020.
[arXiv]
- Abhishake Rastogi, G. Blanchard, P. Mathé.
Convergence analysis of Tikhonov regularization for non-linear
statistical inverse learning problems.
Electronic Journal of Statistics 14 (2): 2798-2841, 2020.
[arXiv]
- G. Blanchard, O. Zadorozhnyi.
Concentration of weakly dependent Banach-valued sums and applications to kernel learning methods.
Bernoulli, 25(4B): 3421-3458, 2019.
[arXiv]
- J. Katz-Samuels, G. Blanchard, C. Scott.
Decontamination of mutual contamination models.
Journal of Machine Learning Research 20 (41):1-57, 2019.
[JMLR]
- G. Blanchard, N. Mücke.
Parallelizing spectral algorithms for kernel learning.
Journal of Machine Learning Research 19 (30):1-29, 2018.
[JMLR]
- G. Blanchard, A. Carpentier, M. Gutzeit.
Minimax Euclidean separation rates for testing convex hypotheses in Rd.
Electron. J. Statist. 12 (2): 3713-3735, 2018.
[Project Euclid Open Access]
- F. Bachoc, G. Blanchard, P. Neuvial.
On the post selection inference constant under restricted isometry properties.
Electron. J. Statist. 12(2): 3736-3757, 2018.
[DOI, Project Euclid Open Access]
- G. Blanchard, M. Hoffmann, M. Reiß.
Early stopping for statistical inverse problems via truncated SVD estimation.
Electron. J. Statist. 12 (2):3204-3231, 2018.
[DOI, Project Euclid Open Access]
- G. Blanchard, M. Hoffmann, M. Reiß.
Optimal adaptation for early stopping in statistical inverse problems.
SIAM/ASA Journal on Uncertainty Quantification 6(3): 1043-1075, 2018.
[DOI]
[arXiv]
- G. Blanchard, F. Göbel, U. von Luxburg.
Construction of tight frames on graphs and application to denoising.
In Handbook of Big Data Analytics, Härdle, W., Lu, H.-S. and Xen, S. editors,
Chapter 20, pp. 503-522, Springer, 2018.
[DOI]
[arXiv]
- G. Blanchard, N. Mücke.
Optimal rates for regularization of statistical inverse learning problems.
Foundations of Computational Mathematics 18 (4): 971-1013, 2018 (first online: 2017).
[DOI]
[arXiv]
- G. Blanchard, N. Krämer.
Convergence rates of Kernel Conjugate Gradient for
random design regression.
Analysis and Applications 14 (6): 763-794, 2016.
[DOI]
[arXiv]
- B. Mieth, M. Kloft, J. A. Rodríguez, S. Sonnenburg, R. Vobruba, C. Morcillo-Suárez, X. Farré, U.M. Marigorta, E. Fehr, T. Dickhaus, G. Blanchard, D. Schunk, A. Navarro & K.-R. Müller.
Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies.
Scientific Reports 6: 36671, 2016.
[DOI, Nature Open Access]
- G. Blanchard, M. Flaska, G. Handy, S. Pozzi, C. Scott.
Classification with
asymmetric label noise: Consistency and maximal denoising.
Electronic Journal of Statistics 10 (2): 2780-2824, 2016.
[Project Euclid Open access]
With corrigendum:
Electronic Journal of Statistics 12 (1): 1779-1781, 2018.
[Project Euclid Open access]
- A. Beinrucker, Ü. Dogan, G. Blanchard.
Extensions of stability selection using subsamples of observations and covariates.
Statistics and Computing 26: 1059-1077, 2016 (First online 2015).
[DOI]
[arXiv]
- G. Blanchard, S. Delattre, E. Roquain.
Testing over a continuum of null hypotheses with false discovery rate control.
Bernoulli 20(1): 304-333, 2014.
[DOI, Open Access]
- G. Blanchard, T. Dickhaus, E. Roquain, F. Villers .
On least favorable configurations for step-up-down tests.
Statistica Sinica 24(1): 1-23, 2014.
[DOI]
[Supplement]
[arXiv]
- G. Blanchard, P. Mathé.
Discrepancy principle for statistical inverse problems with application to conjugate gradient iteration.
Inverse Problems 28 (11): 115011, 2012.
[DOI]
[UPotsdam preprint]
- M. Kloft, G. Blanchard.
The local Rademacher complexity of lp-norm multiple kernel learning.
Journal of Machine Learning Research 3: 2465-2502, 2012.
[JMLR]
[arXiv]
- G. Blanchard, P. Mathé.
Conjugate gradient regularization under general smoothness and noise
assumptions. Journal of Inverse and Ill-posed Problems 18(6):
701-726, 2010.
[DOI]
- G. Blanchard, G. Lee, C. Scott.
Semi-supervised novelty detection. Journal of Machine Learning Research
11(Nov): 2973-3009, 2010.
[JMLR]
- S. Arlot, G. Blanchard, E. Roquain.
Some non-asymptotic results on resampling in high dimension, I:
Confidence regions. Annals of
Statistics 38(1): 51-82, 2010.
[arXiv]
- S. Arlot, G. Blanchard, E. Roquain.
Some non-asymptotic results on resampling in high dimension, II:
Multiple tests. Annals of
Statistics 38(1): 83-99, 2010.
[arXiv]
- G. Blanchard, E. Roquain.
Adaptive FDR control under independence and dependence.
Journal of Machine Learning Research 10:2837-2871, 2009.
[JMLR]
- A. Schwaighofer, T. Schröter, S. Mika,
G. Blanchard.
How wrong can we get? A review of machine learning approaches and error bars.
Combinatorial Chemistry & High Throughput Screening , 12 (5):
453-468, 2009.
[DOI]
- G. Blanchard, E. Roquain.
Two simple sufficient conditions for FDR control. Electronic Journal of
Statistics, 2: 963-992, 2008.
[Project Euclid Open Access]
- G. Blanchard, L. Zwald.
Finite dimensional projection for classification and
statistical learning. IEEE transactions on Information Theory, 54
(9): 4169-4182, 2008.
[DOI]
- G. Blanchard, O. Bousquet,
P. Massart. Statistical performance of support vector
machines.
Annals of Statistics, 36 (2): 489-531, 2008.
[arXiv]
- M. Sugiyama, M. Kawanabe, G. Blanchard,
K.-R. Müller. Approximating the best linear
unbiased estimator of non-Gaussian signals with Gaussian noise.
IEICE Transactions on Information and Systems,
E91-D (5): 1577-1580, 2008.
- G. Blanchard, C. Schäfer,
Y. Rozenholc, K-R. Müller. Optimal dyadic
decision trees. Machine Learning, 66(2-3): 209-242, 2007.
- M. Kawanabe, M. Sugiyama, G. Blanchard, K-R. Müller.
A new algorithm of non-Gaussian component analysis with
radial kernel functions.
Annals of the Institute of Statistical Mathematics, 59(1):57-75,
2007.
- G. Blanchard, O. Bousquet,
L. Zwald. Statistical properties of kernel principal
component analysis. Machine Learning, 66(2-3): 259-294, 2007.
- G. Blanchard, P. Massart.
Discussion of V.Koltchinskii's 2004 IMS Medallion Lecture
paper, "Local Rademacher complexities and oracle inequalities
in risk minimization". Annals of
Statistics , 34(6), 2006.
[arXiv]
- G. Blanchard, M. Kawanabe,
M. Sugiyama, V. Spokoiny,
K.-R. Müller.
In search of non-Gaussian components
of a high-dimensional distribution.
Journal of Machine Learning Research, 7:247-282, 2006.
[JMLR]
- G. Blanchard, D. Geman.
Hierarchical testing designs for pattern recognition.
Annals of Statistics, 33(3):1155-1202, 2005. (This is a shortened and
revised version of the technical report below).
[arXiv]
- G. Blanchard. Different paradigms for
choosing sequential reweighting algorithms. Neural Computation,
16:811-836, 2004.
- G. Blanchard, B. Blankertz. BCI
competition 2003 - data set IIa: Spatial patterns of self-controlled
brain rhythm modulations. IEEE Trans. Biomed. Eng.,
51(6):1062-1066, 2004.
- G. Blanchard. Un algorithme
accéléré d'échantillonnage Bayésien
pour le modèle CART. Revue d'Intelligence artificielle,
18(3):383-410, 2004.
English version (somewhat outdated): A new algorithm for MCMC bayesian CART sampling.
[gzipped
ps]
- G. Blanchard, G. Lugosi, N. Vayatis.
On the rate of convergence of regularized boosting classifiers. Journal
of Machine Learning Research (Special issue on learning theory),
4:861-894, 2003.
[JMLR]
- G. Blanchard. Generalization error
bounds for aggregate classifiers. In Nonlinear Estimation and
Classification, Denison, D. D. ,
Hansen, M. , Holmes, C. C. , Mallick, B.
and Yu, B . editors, Lectures notes in Statistics (171),
Springer, 357-368, 2003.
- G. Blanchard, M. Olsen. Le
système des renvois dans l'Encyclopédie : une
cartographie des structures de connaissance au XVIIIème
siècle. Recherches sur Diderot et l'Encyclopédie,
45-70, 2002.
[Site of RDE, full PDF]
- G. Blanchard. The "progressive
mixture" estimator for regression trees. Annales de l'I.H.P.,
35(6):793-820, 1999.
[NUMDAM link]
- G. Blanchard. L'estimateur de
«mélange progressif» appliqué aux arbres de
décision. C.R.A.S.,328, Série I:925-928,
1999.
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Conference Proceedings (peer
reviewed)
- U. Gazin, G. Blanchard, E. Roquain.
Transductive conformal inference with adaptive scores.
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024) , PMLR 238:1504-1512, 2024.
[PMLR]
[arXiv]
- E.M. Saad, G. Blanchard, N. Verzelen.
Covariance-adaptive best arm identification
Advances in Neural Information Processing Systems 36 (NeurIPS 2023),
p. 73287-73298, Curran Associates, 2023.
[NeurIPS]
[arXiv]
- B. Dussap, G. Blanchard, B.-E. Chérief-Abdellatif.
Label shift quantification with robustness guarantees via distribution feature matching.
Machine Learning and Knowledge Discovery in Databases: Research Track (ECML/PKDD 2023),
Part V, (LNCS, volume 14173), p. 69-85, Springer, 2023.
[arXiv]
- E. Saad, G. Blanchard.
Constant regret for sequence prediction with limited advice.
Algorithmic Learning Theory (ALT 2023).
[HAL]
- E. Saad, G. Blanchard.
Fast rates for prediction with limited expert advice.
Advances in Neural Information Processing Systems (NeurIPS 2021).
[HAL]
- H. Marienwald, J-B. Fermanian, G. Blanchard.
High-Dimensional Multi-Task Averaging and Application to Kernel Mean Embedding.
Artificial Intelligence and Statistics (AISTATS 2021).
[arXiv]
- J. Achddou, J. Lam, A. Carpentier, G. Blanchard
A minimax near-optimal algorithm for adaptive rejection sampling.
Algorithmic Learning Theory (ALT 2019).
[arXiv]
- I. Tolstikhin, N. Zhivotovskiy, G. Blanchard
Permutational Rademacher complexity.
Algorithmic Learning Theory (Proc. ALT 2015), Springer Lecture Notes in Artificial Intelligence (9355), 209-223, 2015.
[arXiv]
- S. Kurras, U. von Luxburg, G. Blanchard
The f-adjusted graph Laplacian: a diagonal modification with a geometric interpretation.
Proc. ICML 2014, JMLR Workshop and
Conference Proceedings 32:1530-1538, 2014.
[JMLR]
- I. Tolstikhin, G. Blanchard, M. Kloft
Localized complexities for transductive learning.
Proc. COLT 2014, JMLR Workshop and
Conference Proceedings 35: 857-884, 2014.
[JMLR]
- G. Blanchard, C. Scott.
Decontamination of mutually contaminated models.
Proc. AISTATS 2014, JMLR Workshop and
Conference Proceedings 33:1-9, 2014.
[JMLR]
- C. Scott, G. Blanchard, G. Handy.
Classification with asymmetric label noise: consistency and maximal denoising.
Proc. Conf. on Learning Theory (COLT 2013),
JMLR Workshop and Conference Proceedings 30:489-511, 2013.
[JMLR]
- R. Martinez-Noriega, A. Roumy, G. Blanchard.
Exemplar-based image inpainting:
Fast priority and coherent nearest neighbor search.
IEEE International Workshop on
Machine Learning for Signal Processing (MLSP 2012) , 2012.
- A. Beinrucker, U. Dogan, G. Blanchard.
Early stopping for mutual information
based feature selection.
International Conference on Pattern Recognition (ICPR 2012) ,
975-978, 2012.
- A. Beinrucker, U. Dogan, G. Blanchard.
A simple extension of stability feature selection.
Pattern Recognition (Proceedings of the joint 34th DAGM and 36th OAGM Symposium), 256-265, 2012.
- G. Blanchard, G. Lee, C. Scott.
Generalizing from several related classification tasks to a new unlabeled sample.
advances in neural inf. proc. systems (nips 2011), 2178-2186, 2011.
[nips proceedings]
- M. Kloft, G. Blanchard.
The local Rademacher complexity of lp-norm multiple kernel learning.
Advances in Neural Inf. Proc. Systems 24 (NIPS 2011), 2438-2446, 2011.
[NIPS proceedings]
- G. Blanchard, N. Krämer.
Optimal learning rates for Kernel Conjugate Gradient regression.
Advances in Neural Inf. Proc. Systems (NIPS 2010), 226-234, 2011.
[NIPS proceedings]
- G. Blanchard, T. Dickhaus, N. Hack, F. Konietschke,
K. Rohmeyer, J. Rosenblatt, M. Scheer, W. Werft.
μTOSS - Multiple hypothesis testing in an open software system.
Proceedings of the First Workshop on Applications of Pattern Analysis,
JMLR Workshop and Conference Proceedings 11:12-19, 2010.
[JMLR]
- G. Blanchard, N. Krämer.
Kernel partial least squares is universally consistent.
AISTATS 2010,
JMLR Workshop and
Conference Proceedings 9:57-64, 2010.
[JMLR]
- C. Scott, G. Blanchard.
Novelty detection: unlabeled data definitely help. AISTATS 2009,
JMLR Workshop and
Conference Proceedings 5:464-471, 2009.
[JMLR]
- G. Blanchard,
F. Fleuret.
Occam's Hammer: a link between randomized learning and multiple testing FDR control.
Proceedings of the 20th. conference on learning theory
(COLT 2007), Springer Lecture Notes on Computer Science
(4539), 112-126, 2007.
[HAL]
- S. Arlot, G. Blanchard,
E. Roquain.
Resampling-based
confidence regions and multiple tests for a correlated random vector.
Proceedings of the 20th. conference on learning theory
(COLT 2007), Springer Lecture Notes on Computer Science
(4539), 127-141, 2007.
- M. Kawanabe, G. Blanchard,
M. Sugiyama, V. Spokoiny, K.-R. Müller.
A novel dimension reduction procedure for searching
non-Gaussian subspaces.
Independent Component Analysis and Blind Signal
Separation (ICA 06), Springer Lecture Notes on Computer Science
(3889), 149-156, 2006.
- M. Sugiyama, M. Kawanabe, G. Blanchard,
V. Spokoiny, K.-R. Müller.
Obtaining the best linear unbiased estimator of noisy
signals by non-Gaussian component analysis.
IEEE International Conference on Acoustics, Speech,
and Signal Processing (ICACSSP 06),
volume 3, pp.608-611, 2006.
- L. Zwald, G. Blanchard.
On the convergence of eigenspaces in kernel principal
components analysis. In
Advances in Neural Inf. Proc. Systems (NIPS 05),
volume 18, 1649-1656, MIT Press, 2006.
[NIPS Proceedings]
- F. Fleuret, G. Blanchard.
Pattern recognition from one example via chopping. In
Advances in Neural Inf. Proc. Systems (NIPS 05),
volume 18, 371-378, MIT Press, 2006.
[NIPS Proceedings]
- G. Blanchard, M. Kawanabe,
M. Sugiyama, V. Spokoiny, K.-R. Müller.
Non-Gaussian component analysis: a semi-parametric
framework for linear dimension reduction. In
Advances in Neural Inf. Proc. Systems (NIPS 05),
volume 18, 131-138, MIT Press, 2006.
[NIPS Proceedings]
- G. Blanchard, P. Massart, R. Vert, L. Zwald.
Kernel Projection Machine: a new tool for pattern recognition.
In Advances in Neural Inf. Proc. Systems (NIPS
2004), volume 17, 1649-1656, MIT Press, 2005.
[NIPS Proceedings]
- G. Blanchard, C. Schäfer,
Y. Rozenholc. Oracle bounds and exact algorithm for dyadic
classification trees. In Proceedings of the 17th. Conference on
Learning Theory (COLT 04), Springer Lecture
Notes in Artificial Intelligence (3120), 378-392, 2004.
- O. Bousquet, L. Zwald, G. Blanchard.
Statistical properties of kernel principal component analysis. In Proceedings
of the 17th. Conference on Learning Theory (COLT 2004).
Springer Lecture Notes in Artificial Intelligence (3120), 594-608,
2004.
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