Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
Bibliography
XML PDF e-pub
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Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

[1]
D. Babichev.
On Efficient Methods for High-dimensional Statistical Estimation, PSL Research University, February 2019.
https://hal.archives-ouvertes.fr/tel-02433016
[2]
T. Shpakova.
On Parameter Learning for Perturb-and-MAP Models, PSL Research University, February 2019.
https://hal.archives-ouvertes.fr/tel-02431640

Articles in International Peer-Reviewed Journals

[3]
P. Askenazy, F. Bach.
IA et emploi : Une menace artificielle, in: Pouvoirs - Revue française d’études constitutionnelles et politiques, September 2019, vol. 170, pp. 1-7.
https://hal.archives-ouvertes.fr/hal-02343633
[4]
M. Brégère, P. Gaillard, Y. Goude, G. Stoltz.
Target Tracking for Contextual Bandits: Application to Demand Side Management, in: Proceedings of Machine Learning Research, June 2019, vol. 97, pp. 754-763, https://arxiv.org/abs/1901.09532.
https://hal.archives-ouvertes.fr/hal-01994144
[5]
Y. Drori, A. B. Taylor.
Efficient First-order Methods for Convex Minimization: a Constructive Approach, in: Mathematical Programming, Series A, 2019, https://arxiv.org/abs/1803.05676 - Code available at https://github.com/AdrienTaylor/GreedyMethods, forthcoming. [ DOI : 10.1007/s10107-019-01410-2 ]
https://hal.inria.fr/hal-01902048
[6]
P. Gaillard, S. Gerchinovitz, M. Huard, G. Stoltz.
Uniform regret bounds over Rd for the sequential linear regression problem with the square loss, in: Proceedings of Machine Learning Research, 2019, vol. 98, pp. 404-432, https://arxiv.org/abs/1805.11386.
https://hal.archives-ouvertes.fr/hal-01802004
[7]
J.-L. Peyrot, L. Duval, F. Payan, L. Bouard, L. Chizat, S. Schneider, M. Antonini.
HexaShrink, an exact scalable framework for hexahedral meshes with attributes and discontinuities: multiresolution rendering and storage of geoscience models, in: Computational Geosciences, August 2019, vol. 23, no 4, pp. 723-743, https://arxiv.org/abs/1903.07614. [ DOI : 10.1007/s10596-019-9816-2 ]
https://hal-ifp.archives-ouvertes.fr/hal-01857997
[8]
V. Roulet, N. Boumal, A. D'Aspremont.
Computational complexity versus statistical performance on sparse recovery problems, in: Information and Inference, January 2019, https://arxiv.org/abs/1506.03295. [ DOI : 10.1093/imaiai/iay020 ]
https://hal.archives-ouvertes.fr/hal-02340337

International Conferences with Proceedings

[9]
A. Bietti, G. Mialon, D. Chen, J. Mairal.
A Kernel Perspective for Regularizing Deep Neural Networks, in: ICML 2019 - 36th International Conference on Machine Learning, Long Beach, United States, Proceedings of Machine Learning Research, June 2019, vol. 97, pp. 664-674, https://arxiv.org/abs/1810.00363.
https://hal.inria.fr/hal-01884632
[10]
R. Bollapragada, D. Scieur, A. D'Aspremont.
Nonlinear Acceleration of Momentum and Primal-Dual Algorithms, in: AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics, Naha, Japan, The 22nd International Conference on Artificial Intelligence and Statistics,, April 2019, vol. 89, https://arxiv.org/abs/1810.04539. [ DOI : 10.04539 ]
https://hal.archives-ouvertes.fr/hal-01893921
[11]
T. Kerdreux, A. D'Aspremont, S. Pokutta.
Restarting Frank-Wolfe, in: AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics, Naha, Japan, Proceedings of Machine Learning Research, April 2019, vol. 89, https://arxiv.org/abs/1810.02429. [ DOI : 10.02429 ]
https://hal.archives-ouvertes.fr/hal-01893922
[12]
U. Marteau-Ferey, F. Bach, A. Rudi.
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses, in: NeurIPS 2019 - Conference on Neural Information Processing Systems, Vancouver, Canada, December 2019, https://arxiv.org/abs/1907.01771.
https://hal.inria.fr/hal-02169626
[13]
T. Ryffel, E. Dufour-Sans, R. Gay, F. Bach, D. Pointcheval.
Partially Encrypted Machine Learning using Functional Encryption, in: NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, Vancouver, Canada, Advances in Neural Information Processing Systems, December 2019, https://arxiv.org/abs/1905.10214.
https://hal.inria.fr/hal-02357181
[14]
A. Taylor, F. Bach.
Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions, in: COLT 2019 - Conference on Learning Theory, Phoenix, United States, June 2019, https://arxiv.org/abs/1902.00947 - 12 pages + appendix; code available at https://github.com/AdrienTaylor/Potential-functions-for-first-order-methods.
https://hal.inria.fr/hal-02009309

Conferences without Proceedings

[15]
P. Ablin, A. Gramfort, J.-F. Cardoso, F. Bach.
Stochastic algorithms with descent guarantees for ICA, in: AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics, Naha, Japan, April 2019.
https://hal.archives-ouvertes.fr/hal-02372092
[16]
L. Chizat, E. Oyallon, F. Bach.
On Lazy Training in Differentiable Programming, in: NeurIPS 2019 - 33rd Conference on Neural Information Processing Systems, Vancouver, Canada, December 2019, https://arxiv.org/abs/1812.07956.
https://hal.inria.fr/hal-01945578
[17]
A. Genevay, L. Chizat, F. Bach, M. Cuturi, G. Peyré.
Sample Complexity of Sinkhorn divergences, in: AISTATS'19 - 22nd International Conference on Artificial Intelligence and Statistics, Okinawa, Japan, K. Chaudhuri, M. Sugiyama (editors), April 2019, https://arxiv.org/abs/1810.02733. [ DOI : 10.02733 ]
https://hal.archives-ouvertes.fr/hal-02411822
[18]
R. M. Gower, N. Loizou, X. Qian, A. Sailanbayev, E. Shulgin, P. Richtárik.
SGD: General Analysis and Improved Rates, in: International Conference on Machine Learning, Los Angeles, United States, June 2019.
https://hal.telecom-paristech.fr/hal-02365318
[19]
D. M. Ostrovskii, A. Rudi.
Affine Invariant Covariance Estimation for Heavy-Tailed Distributions, in: COLT 2019 - 32nd Annual Conference on Learning Theory, Phoenix, United States, June 2019, https://arxiv.org/abs/1902.03086.
https://hal.archives-ouvertes.fr/hal-02011464
[20]
A. Podosinnikova, A. Perry, A. Wein, F. Bach, A. D'Aspremont, D. Sontag.
Overcomplete Independent Component Analysis via SDP, in: AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics, Naha, Japan, April 2019, https://arxiv.org/abs/1901.08334 - Appears in: Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019). 21 pages.
https://hal.archives-ouvertes.fr/hal-02340366

Other Publications

[21]
A. Askari, A. D'Aspremont, L. E. Ghaoui.
Naive Feature Selection: Sparsity in Naive Bayes, October 2019, https://arxiv.org/abs/1905.09884 - working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02340374
[22]
D. Babichev, D. M. Ostrovskii, F. Bach.
Efficient Primal-Dual Algorithms for Large-Scale Multiclass Classification, February 2019, https://arxiv.org/abs/1902.03755 - working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02012569
[23]
F. Bach.
Max-Plus Matching Pursuit for Deterministic Markov Decision Processes, June 2019, https://arxiv.org/abs/1906.08524 - working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02155865
[24]
M. Barré, A. D'Aspremont.
Polyak Steps for Adaptive Fast Gradient Method, October 2019, https://arxiv.org/abs/1906.03056 - working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02340373
[25]
R. Berthier, F. Bach, P. Gaillard.
Accelerated Gossip in Networks of Given Dimension using Jacobi Polynomial Iterations, February 2019, https://arxiv.org/abs/1805.08531 - working paper or preprint.
https://hal.archives-ouvertes.fr/hal-01797016
[26]
A. D'Aspremont, M. Cucuringu, H. Tyagi.
Ranking and synchronization from pairwise measurements via SVD, October 2019, https://arxiv.org/abs/1906.02746 - 42 pages, 9 figures.
https://hal.archives-ouvertes.fr/hal-02340372
[27]
R.-A. Dragomir, A. D'Aspremont, J. Bolte.
Quartic First-Order Methods for Low Rank Minimization, October 2019, https://arxiv.org/abs/1901.10791 - working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02340369
[28]
R.-A. Dragomir, A. Taylor, A. D'Aspremont, J. Bolte.
Optimal Complexity and Certification of Bregman First-Order Methods, November 2019, https://arxiv.org/abs/1911.08510 - working paper or preprint.
https://hal.inria.fr/hal-02384167
[29]
A. Défossez, N. Usunier, L. Bottou, F. Bach.
Demucs: Deep Extractor for Music Sources with extra unlabeled data remixed, September 2019, https://arxiv.org/abs/1909.01174 - working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02277338
[30]
A. Défossez, N. Usunier, L. Bottou, F. Bach.
Music Source Separation in the Waveform Domain, November 2019, https://arxiv.org/abs/1911.13254 - working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02379796
[31]
H. Hendrikx, F. Bach, L. Massoulié.
An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums, September 2019, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02280763
[32]
R. Jézéquel, P. Gaillard, A. Rudi.
Efficient online learning with kernels for adversarial large scale problems, May 2019, https://arxiv.org/abs/1902.09917 - working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02019402
[33]
U. Marteau-Ferey, D. M. Ostrovskii, F. Bach, A. Rudi.
Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance, June 2019, https://arxiv.org/abs/1902.03046 - working paper or preprint.
https://hal.inria.fr/hal-02011895
[34]
G. Mialon, A. D'Aspremont, J. Mairal.
Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Functions, December 2019, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02395624
[35]
F.-P. Paty, A. D'Aspremont, M. Cuturi.
Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport, October 2019, https://arxiv.org/abs/1905.10812 - working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02340371
[36]
L. Pillaud-Vivien, F. Bach, T. Lelièvre, A. Rudi, G. Stoltz.
Statistical Estimation of the Poincaré constant and Application to Sampling Multimodal Distributions, November 2019, https://arxiv.org/abs/1910.14564 - working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02327453
[37]
T.-H. Vu, A. Osokin, I. Laptev.
Tube-CNN: Modeling temporal evolution of appearance for object detection in video, January 2019, https://arxiv.org/abs/1812.02619 - 13 pages, 8 figures, technical report.
https://hal.archives-ouvertes.fr/hal-01980339