Bibliography

Publications of the year

Articles in International Peer-Reviewed Journals

• 1J.-B. Alayrac, P. Bojanowski, N. Agrawal, J. Sivic, I. Laptev, S. Lacoste-Julien.
  
  Learning from narrated instruction videos, in: IEEE Transactions on Pattern Analysis and Machine Intelligence, September 2017, vol. XX.
  
  https://hal.archives-ouvertes.fr/hal-01580630
• 2F. Bach.
  
  On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions, in: Journal of Machine Learning Research, 2017, vol. 18, n^o 21, pp. 1-38, https://arxiv.org/abs/1502.06800.
  
  https://hal.archives-ouvertes.fr/hal-01118276
• 3F. Pedregosa, F. Bach, A. Gramfort.
  
  On the Consistency of Ordinal Regression Methods, in: Journal of Machine Learning Research, 2017, vol. 18, pp. 1 - 35, https://arxiv.org/abs/1408.2327.
  
  https://hal.inria.fr/hal-01054942
• 4N. P. Rougier, K. Hinsen, F. Alexandre, T. Arildsen, L. Barba, F. C. Y. Benureau, C. T. Brown, P. De Buyl, O. Caglayan, A. P. Davison, M. A. Delsuc, G. Detorakis, A. K. Diem, D. Drix, P. Enel, B. Girard, O. Guest, M. G. Hall, R. N. Henriques, X. Hinaut, K. S. Jaron, M. Khamassi, A. Klein, T. Manninen, P. Marchesi, D. McGlinn, C. Metzner, O. L. Petchey, H. E. Plesser, T. Poisot, K. Ram, Y. Ram, E. Roesch, C. Rossant, V. Rostami, A. Shifman, J. Stachelek, M. Stimberg, F. Stollmeier, F. Vaggi, G. Viejo, J. Vitay, A. Vostinar, R. Yurchak, T. Zito.
  
  Sustainable computational science: the ReScience initiative, in: PeerJ Computer Science, 2017, https://arxiv.org/abs/1707.04393 - 8 pages, 1 figure, forthcoming.
  
  https://hal.inria.fr/hal-01592078

Articles in Non Peer-Reviewed Journals

• 5A. Meurer, C. Smith, M. Paprocki, O. Čertík, S. Kirpichev, M. Rocklin, A. Kumar, S. Ivanov, J. Moore, S. Singh, T. Rathnayake, S. Vig, B. Granger, R. Muller, F. Bonazzi, H. Gupta, S. Vats, F. Johansson, F. Pedregosa, M. Curry, A. Terrel, Š. Roučka, A. Saboo, I. Fernando, S. Kulal, R. Cimrman, A. Scopatz.
  
  SymPy: symbolic computing in Python, in: PeerJ Comput.Sci., 2017, vol. 3, e103 p. [ DOI : 10.7717/peerj-cs.103 ]
  
  https://hal.archives-ouvertes.fr/hal-01645958

Invited Conferences

• 6R. S. Rezende, J. Zepeda, J. S. Ponce, F. S. Bach, P. Perez.
  
  Kernel Square-Loss Exemplar Machines for Image Retrieval, in: Computer Vision and Pattern Recognition 2017, Honolulu, United States, Computer vision and pattern recognition 2017, July 2017.
  
  https://hal.inria.fr/hal-01515224

International Conferences with Proceedings

• 7A. Beaugnon, P. Chifflier, F. Bach.
  
  ILAB: An Interactive Labelling Strategy for Intrusion Detection, in: RAID 2017: Research in Attacks, Intrusions and Defenses, Atlanta, United States, September 2017.
  
  https://hal.archives-ouvertes.fr/hal-01636299
• 8P. Gaillard, O. Wintenberger.
  
  Sparse Accelerated Exponential Weights, in: 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, United States, April 2017.
  
  https://hal.archives-ouvertes.fr/hal-01376808
• 9R. Leblond, F. Pedregosa, S. Lacoste-Julien.
  
  Asaga: Asynchronous Parallel Saga, in: 20th International Conference on Artificial Intelligence and Statistics (AISTATS) 2017, Fort Lauderdale, Florida, United States, April 2017.
  
  https://hal.inria.fr/hal-01665255
• 10F. Pedregosa, R. Leblond, S. Lacoste-Julien.
  
  Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization, in: NIPS 2017 - Thirty-First Annual Conference on Neural Information Processing Systems, Long Beach, United States, December 2017, pp. 1-28, https://arxiv.org/abs/1707.06468 - Appears in Advances in Neural Information Processing Systems 30 (NIPS 2017), 28 pages.
  
  https://hal.inria.fr/hal-01638058
• 11F. Pedregosa, R. Leblond, S. Lacoste-Julien.
  
  Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization, in: 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, California, United States, December 2017.
  
  https://hal.inria.fr/hal-01665260

National Conferences with Proceedings

• 12A. Goyal, E. Morvant, P. Germain.
  
  Une borne PAC-Bayésienne en espérance et son extension à l'apprentissage multivues, in: Conférence Francophone sur l'Apprentissage Automatique (CAp), Grenoble, France, June 2017.
  
  https://hal.archives-ouvertes.fr/hal-01529219

Conferences without Proceedings

• 13A. Beaugnon, A. Husson.
  
  Le Machine Learning confronté aux contraintes opérationnelles des systèmes de détection, in: SSTIC 2017: Symposium sur la sécurité des technologies de l'information et des communications, Rennes, France, June 2017, pp. 317-346.
  
  https://hal.archives-ouvertes.fr/hal-01636303
• 14G. Gidel, T. Jebara, S. Lacoste-Julien.
  
  Frank-Wolfe Algorithms for Saddle Point Problems, in: The 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, Florida, United States, April 2017, https://arxiv.org/abs/1610.07797.
  
  https://hal.archives-ouvertes.fr/hal-01403348
• 15A. Goyal, E. Morvant, P. Germain, M.-R. Amini.
  
  PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach, in: European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Skopje, Macedonia, September 2017, Long version : https://arxiv.org/abs/1606.07240.
  
  https://hal.archives-ouvertes.fr/hal-01546109
• 16A. Osokin, F. Bach, S. Lacoste-Julien.
  
  On Structured Prediction Theory with Calibrated Convex Surrogate Losses, in: The Thirty-first Annual Conference on Neural Information Processing Systems (NIPS), Long Beach, United States, December 2017, https://arxiv.org/abs/1703.02403.
  
  https://hal.archives-ouvertes.fr/hal-01611691
• 17A. Osokin, A. Chessel, R. E. C. Salas, F. Vaggi.
  
  GANs for Biological Image Synthesis, in: ICCV 2017 - IEEE International Conference on Computer Vision, Venice, Italy, October 2017, https://arxiv.org/abs/1708.04692.
  
  https://hal.archives-ouvertes.fr/hal-01611692

Scientific Books (or Scientific Book chapters)

• 18Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, V. Lempitsky.
  
  Domain-Adversarial Training of Neural Networks, in: Domain Adaptation in Computer Vision Applications, G. Csurka (editor), Advances in Computer Vision and Pattern Recognition, Springer, September 2017. [ DOI : 10.1007/978-3-319-58347-1 ]
  
  https://hal.archives-ouvertes.fr/hal-01624607

Other Publications

• 19F. Bach.
  
  Efficient Algorithms for Non-convex Isotonic Regression through Submodular Optimization, July 2017, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01569934
• 20N. Cesa-Bianchi, P. Gaillard, C. Gentile, S. Gerchinovitz.
  
  Algorithmic Chaining and the Role of Partial Feedback in Online Nonparametric Learning, June 2017, https://arxiv.org/abs/1702.08211 - This document is the full version of an extended abstract accepted for presentation at COLT 2017..
  
  https://hal.archives-ouvertes.fr/hal-01476771
• 21A. Dieuleveut, A. Durmus, F. Bach.
  
  Bridging the Gap between Constant Step Size Stochastic Gradient Descent and Markov Chains, July 2017, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01565514
• 22A. Défossez, F. Bach.
  
  AdaBatch: Efficient Gradient Aggregation Rules for Sequential and Parallel Stochastic Gradient Methods, November 2017, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01620513
• 23N. Flammarion, F. Bach.
  
  Stochastic Composite Least-Squares Regression with convergence rate O(1/n), February 2017, working paper or preprint.
  
  https://hal.inria.fr/hal-01472867
• 24P. Germain, A. Habrard, F. Laviolette, E. Morvant.
  
  PAC-Bayes and Domain Adaptation, July 2017, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01563152
• 25R. M. Gower, P. Richtárik.
  
  Linearly Convergent Randomized Iterative Methods for Computing the Pseudoinverse, January 2017, 28 pages, 10 figures.
  
  https://hal.inria.fr/hal-01430489
• 26R. M. Gower, N. L. Roux, F. Bach.
  
  Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods, November 2017, https://arxiv.org/abs/1710.07462 - 17 pages, 2 figures, 1 table. [ DOI : 10.07462 ]
  
  https://hal.archives-ouvertes.fr/hal-01652152
• 27A. Goyal, E. Morvant, P. Germain, M.-R. Amini.
  
  PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach, July 2017, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01336260
• 28M. E. Halabi, F. Bach, V. Cevher.
  
  Combinatorial Penalties: Which structures are preserved by convex relaxations?, November 2017, working paper or preprint. [ DOI : 10.06273 ]
  
  https://hal.archives-ouvertes.fr/hal-01652151
• 29A. Kundu, F. Bach, C. Bhattacharyya.
  
  Convex optimization over intersection of simple sets: improved convergence rate guarantees via an exact penalty approach, November 2017, working paper or preprint. [ DOI : 10.06465 ]
  
  https://hal.archives-ouvertes.fr/hal-01652149
• 30R. Leblond, J.-B. Alayrac, A. Osokin, S. Lacoste-Julien.
  
  SEARNN: Training RNNs with global-local losses, December 2017, https://arxiv.org/abs/1706.04499 - 12 pages.
  
  https://hal.inria.fr/hal-01665263
• 31L. Pillaud-Vivien, A. Rudi, F. Bach.
  
  Exponential convergence of testing error for stochastic gradient methods, December 2017, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01662278
• 32S. J. Reddi, M. Zaheer, S. Sra, B. Poczos, F. Bach, R. Salakhutdinov, A. J. Smola.
  
  A Generic Approach for Escaping Saddle points, November 2017, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01652150
• 33V. Roulet, A. d'Aspremont.
  
  Sharpness, Restart and Acceleration, February 2017, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01474362
• 34V. Roulet, F. Fogel, A. D'Aspremont, F. Bach.
  
  Iterative hard clustering of features, December 2017, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01664964
• 35K. Scaman, F. Bach, S. Bubeck, Y. T. Lee, L. Massoulié.
  
  Optimal algorithms for smooth and strongly convex distributed optimization in networks, February 2017, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01478317
• 36D. Scieur, A. d'Aspremont, F. Bach.
  
  Nonlinear Acceleration of Stochastic Algorithms, October 2017, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01618379
• 37D. Scieur, V. Roulet, F. Bach, A. d'Aspremont.
  
  Integration Methods and Accelerated Optimization Algorithms, February 2017, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01474045
• 38J. Tang, F. Bach, M. Golbabaee, M. E. Davies.
  
  Structure-Adaptive, Variance-Reduced, and Accelerated Stochastic Optimization, December 2017, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01658487
• 39J. Weed, F. Bach.
  
  Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance, July 2017, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01555307