Team Tau
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
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Bibliography

Major publications by the team in recent years
  • 1C. Adam-Bourdarios, G. Cowan, C. Germain-Renaud, I. Guyon, B. Kégl, D. Rousseau.
    The Higgs Machine Learning Challenge, in: Journal of Physics: Conference Series, December 2015, vol. 664, no 7. [ DOI : 10.1088/1742-6596/664/7/072015 ]
    https://hal.inria.fr/hal-01745998
  • 2L. Da Costa, Á. Fialho, M. Schoenauer, M. Sebag.
    Adaptive Operator Selection with Dynamic Multi-Armed Bandits, in: Proc. Genetic and Evolutionary Computation Conference (GECCO), ACM, 2008, pp. 913-920, ACM-SIGEVO 10-years Impact Award. [ DOI : 10.1145/1389095.1389272 ]
    https://hal.inria.fr/inria-00278542
  • 3C. Furtlehner, A. Decelle.
    Cycle-based Cluster Variational Method for Direct and Inverse Inference, in: Journal of Statistical Physics, August 2016, vol. 164, no 3, pp. 531–574.
    https://hal.inria.fr/hal-01214155
  • 4S. Gelly, M. Schoenauer, M. Sebag, O. Teytaud, L. Kocsis, D. Silver, C. Szepesvari.
    The Grand Challenge of Computer Go: Monte Carlo Tree Search and Extensions, in: Communications- ACM, 2012, vol. 55, no 3, pp. 106-113.
    https://hal.inria.fr/hal-00695370
  • 5O. Goudet, D. Kalainathan, P. Caillou, D. Lopez-Paz, I. Guyon, M. Sebag.
    Learning Functional Causal Models with Generative Neural Networks, in: Explainable and Interpretable Models in Computer Vision and Machine Learning, Springer Series on Challenges in Machine Learning, Springer International Publishing, 2018, https://arxiv.org/abs/1709.05321. [ DOI : 10.1007/978-3-319-98131-4 ]
    https://hal.archives-ouvertes.fr/hal-01649153
  • 6T. Lucas, C. Tallec, J. Verbeek, Y. Ollivier.
    Mixed batches and symmetric discriminators for GAN training, in: ICML - 35th International Conference on Machine Learning, Stockholm, Sweden, July 2018.
    https://hal.inria.fr/hal-01791126
  • 7E. Maggiori, Y. Tarabalka, G. Charpiat, P. Alliez.
    Convolutional Neural Networks for Large-Scale Remote Sensing Image Classification, in: IEEE Transactions on Geoscience and Remote Sensing, 2017, vol. 55, no 2, pp. 645-657.
    https://hal.inria.fr/hal-01369906
  • 8M. Mısır, M. Sebag.
    Alors: An algorithm recommender system, in: Artificial Intelligence, 2017, vol. 244, pp. 291-314, Published on-line Dec. 2016.
    https://hal.inria.fr/hal-01419874
  • 9Y. Ollivier, L. Arnold, A. Auger, N. Hansen.
    Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles, in: Journal of Machine Learning Research, 2017, vol. 18, no 18, pp. 1-65.
    https://hal.inria.fr/hal-01515898
  • 10X. Zhang, C. Furtlehner, C. Germain-Renaud, M. Sebag.
    Data Stream Clustering with Affinity Propagation, in: IEEE Transactions on Knowledge and Data Engineering, 2014, vol. 26, no 7, 1 p.
    https://hal.inria.fr/hal-00862941
Publications of the year

Doctoral Dissertations and Habilitation Theses

Articles in International Peer-Reviewed Journals

  • 13G. Bastin, P. Tubaro.
    Le moment big data des sciences sociales, in: Revue française de sociologie, September 2018, vol. 59, no 3, pp. 375-394. [ DOI : 10.3917/rfs.593.0375 ]
    https://hal.archives-ouvertes.fr/hal-01885416
  • 14K. Caye, F. Jay, O. Michel, O. François.
    Fast Inference of Individual Admixture Coefficients Using Geographic Data, in: Annals Of Applied Statistics, 2018.
    https://hal.archives-ouvertes.fr/hal-01676712
  • 15A. Decelle, G. Fissore, C. Furtlehner.
    Thermodynamics of Restricted Boltzmann Machines and Related Learning Dynamics, in: Journal of Statistical Physics, January 2018, vol. 172, no 6, pp. 1576-1608. [ DOI : 10.1007/s10955-018-2105-y ]
    https://hal.inria.fr/hal-01675310
  • 16S. Escalera, X. Baró, I. Guyon, H. J. Escalante.
    Guest Editorial: Apparent Personality Analysis, in: IEEE Transactions on Affective Computing, January 2018, vol. 9, no 3, pp. 299-302. [ DOI : 10.1007/978-3-319-98131-4 ]
    https://hal.inria.fr/hal-01991636
  • 17S. Escalera, X. Baró, I. Guyon, H. J. Escalante, G. Tzimiropoulos, M. Valstar, M. Pantic, J. Cohn, T. Kanade.
    Guest Editorial: The Computational Face, in: IEEE Transactions on Pattern Analysis and Machine Intelligence, November 2018, vol. 40, no 11, pp. 2541-2545. [ DOI : 10.1109/tpami.2018.2869610 ]
    https://hal.inria.fr/hal-01991654
  • 18F. Pallotti, P. Tubaro, A. A. Casilli, T. W. Valente.
    "You see yourself like in a mirror”: The effects of internet-mediated personal networks on body image and eating disorders, in: Health Communication, 2018, vol. 33, no 9, pp. 1166-1176, Published online on 6 July 2017. [ DOI : 10.1080/10410236.2017.1339371 ]
    https://hal.archives-ouvertes.fr/hal-01520138
  • 19N. Shah, B. Tabibian, K. Muandet, I. Guyon, U. Von Luxburg.
    Design and Analysis of the NIPS 2016 Review Process, in: Journal of Machine Learning Research, 2018, vol. 19, pp. 1 - 34.
    https://hal.inria.fr/hal-01991580
  • 20P. Taillandier, B. Gaudou, A. Grignard, Q.-N. Huynh, N. Marilleau, P. Caillou, D. Philippon, A. Drogoul.
    Building, composing and experimenting complex spatial models with the GAMA platform, in: GeoInformatica, December 2018.
    https://hal.inria.fr/hal-01975984

International Conferences with Proceedings

  • 21M.-L. Cauwet, J. Decock, J. Liu, O. Teytaud.
    Direct Model Predictive Control: A Theoretical and Numerical Analysis, in: PSCC 2018 - XX Power Systems Computation Conference, Dublin, Ireland, June 2018.
    https://hal.inria.fr/hal-01701623
  • 22M.-L. Cauwet, O. Teytaud.
    Surprising strategies obtained by stochastic optimization in partially observable games, in: CEC 2018 - IEEE Congress on Evolutionary Computation, Rio de Janeiro, Brazil, July 2018, pp. 1-8, https://arxiv.org/abs/1807.01877.
    https://hal.inria.fr/hal-01829721
  • 23B. Donnot, I. Guyon, M. Schoenauer, A. Marot, P. Panciatici.
    Anticipating contingengies in power grids using fast neural net screening, in: IEEE International Joint Conference on Neural Networks, Rio de Janeiro, Brazil, Proc. 2018 International Joint Conference on Neural Networks (IJCNN), IEEE, July 2018, https://arxiv.org/abs/1805.02608.
    https://hal.archives-ouvertes.fr/hal-01783669
  • 24B. Donnot, I. Guyon, M. Schoenauer, A. Marot, P. Panciatici.
    Fast Power system security analysis with Guided Dropout, in: 26th European Symposium on Artificial Neural Networks, Bruges, Belgium, Electronic Proceedings ESANN 2018, April 2018, https://arxiv.org/abs/1801.09870.
    https://hal.archives-ouvertes.fr/hal-01695793
  • 25V. Estrade, C. Germain, I. Guyon, D. Rousseau.
    Systematics aware learning: a case study in High Energy Physics, in: ESANN 2018 - 26th European Symposium on Artificial Neural Networks, Bruges, Belgium, April 2018.
    https://hal.inria.fr/hal-01715155
  • 26S. Giffard-Roisin, D. Gagne, A. Boucaud, B. Kégl, M. Yang, G. Charpiat, C. Monteleoni.
    The 2018 Climate Informatics Hackathon: Hurricane Intensity Forecast, in: 8th International Workshop on Climate Informatics, Boulder, CO, United States, PROCEEDINGS OF THE 8TH INTERNATIONAL WORKSHOP ON CLIMATE INFORMATICS: CI 2018, September 2018, 4 p.
    https://hal.archives-ouvertes.fr/hal-01924336
  • 27N. Girard, G. Charpiat, Y. Tarabalka.
    Aligning and Updating Cadaster Maps with Aerial Images by Multi-Task, Multi-Resolution Deep Learning, in: Asian Conference on Computer Vision (ACCV), Perth, Australia, Proceedings, December 2018.
    https://hal.archives-ouvertes.fr/hal-01923568
  • 28T. Lucas, C. Tallec, J. Verbeek, Y. Ollivier.
    Mixed batches and symmetric discriminators for GAN training, in: ICML - 35th International Conference on Machine Learning, Stockholm, Sweden, July 2018.
    https://hal.inria.fr/hal-01791126
  • 29G. Ramírez, E. Villatoro, B. Ionescu, H. J. Escalante, S. Escalera, M. Larson, H. Müller, I. Guyon.
    Overview of the Multimedia Information Processing for Personality and Social Networks Analysis Contest, in: International Conference on Pattern Recognition (ICPR), Beijing, China, T. Tan, J. Kittler, A. Jain (editors), IEEE, August 2018, pp. 127-139.
    https://hal.inria.fr/hal-01991599
  • 30D. Rousseau, S. Amrouche, P. Calafiura, V. Estrade, S. Farrell, C. Germain, V. Gligorov, T. Golling, H. Gray, I. Guyon, M. Hushchyn, V. Innocente, M. Kiehn, A. Salzburger, A. Ustyuzhanin, J.-R. V. Vlimant, Y. Yilmaz.
    The TrackML challenge, in: NIPS 2018 - 32nd Annual Conference on Neural Information Processing Systems, Montreal, Canada, December 2018, pp. 1-23.
    https://hal.inria.fr/hal-01745714
  • 31A. Schoenauer Sebag, L. Heinrich, M. Schoenauer, M. Sebag, L. Wu, S. Altschuler.
    Multi-Domain Adversarial Learning, in: ICLR'19 - Seventh annual International Conference on Learning Representations, New Orleans, United States, Proc. ICLR 2019, Tara Sainath, May 2019.
    https://hal.inria.fr/hal-01968180
  • 32C. Tallec, Y. Ollivier.
    Can recurrent neural networks warp time?, in: International Conference on Learning Representation 2018, Vancouver, France, April 2018.
    https://hal.inria.fr/hal-01812064
  • 33P. Tubaro.
    Emergent relational structures at a "sharing economy" festival, in: The 7th International Conference on Complex Networks and Their Applications, Cambridge, United Kingdom, Springer, December 2018, vol. 2, pp. 559-571.
    https://hal.archives-ouvertes.fr/hal-01947911
  • 34A. Zampieri, G. Charpiat, N. Girard, Y. Tarabalka.
    Multimodal image alignment through a multiscale chain of neural networks with application to remote sensing, in: European Conference on Computer Vision (ECCV), Munich, Germany, V. Ferrari, M. Hebert, C. Sminchisescu, Y. Weiss (editors), Computer Vision – ECCV 2018, Springer International Publishing, September 2018, pp. 679-696.
    https://hal.inria.fr/hal-01849389

Conferences without Proceedings

  • 35S. Giffard-Roisin, M. Yang, G. Charpiat, B. Kégl, C. Monteleoni.
    Deep Learning for Hurricane Track Forecasting from Aligned Spatio-temporal Climate Datasets, in: Modeling and decision-making in the spatiotemporal domain NIPS workhop, Montréal, Canada, December 2018.
    https://hal.archives-ouvertes.fr/hal-01905408
  • 36S. Giffard-Roisin, M. Yang, G. Charpiat, B. Kégl, C. Monteleoni.
    Fused Deep Learning for Hurricane Track Forecast from Reanalysis Data, in: Climate Informatics Workshop Proceedings 2018, Boulder, United States, September 2018.
    https://hal.archives-ouvertes.fr/hal-01851001
  • 37Z. Liu, O. Bousquet, A. Elisseeff, S. Escalera, I. Guyon, J. Jacques, A. Pavao, D. Silver, L. Sun-Hosoya, S. Treguer, W.-W. Tu, J. Wang, Q. Yao.
    AutoDL Challenge Design and Beta Tests-Towards automatic deep learning, in: CiML workshop @ NIPS2018, Montreal, Canada, December 2018.
    https://hal.archives-ouvertes.fr/hal-01906226
  • 38J. G. Madrid, H. Jair Escalante, E. F. Morales, W.-W. Tu, Y. Yu, L. Sun-Hosoya, I. Guyon, M. Sebag.
    Towards AutoML in the presence of Drift: first results, in: Workshop AutoML 2018 @ ICML/IJCAI-ECAI, Stockholm, Sweden, Pavel Brazdil, Christophe Giraud-Carrier, and Isabelle Guyon, July 2018.
    https://hal.inria.fr/hal-01966962
  • 39A. Marot, B. Donon, I. Guyon, B. Donnot.
    Learning To Run a Power Network Competition, in: CiML Workshop, NeurIPS, Montréal, Canada, December 2018.
    https://hal.archives-ouvertes.fr/hal-01968295
  • 40H. Rakotoarison, M. Sebag.
    AutoML with Monte Carlo Tree Search, in: Workshop AutoML 2018 @ ICML/IJCAI-ECAI, Stockholm, Sweden, Pavel Brazdil, Christophe Giraud-Carrier, and Isabelle Guyon, July 2018.
    https://hal.inria.fr/hal-01966957
  • 41H. Richard, A. Pinho, B. Thirion, G. Charpiat.
    Optimizing deep video representation to match brain activity, in: CCN 2018 - Conference on Cognitive Computational Neuroscience, Philadelphia, United States, September 2018, https://arxiv.org/abs/1809.02440.
    https://hal.archives-ouvertes.fr/hal-01868735
  • 42L. Sun-Hosoya, I. Guyon, M. Sebag.
    Lessons learned from the AutoML challenge, in: Conférence sur l'Apprentissage Automatique 2018, Rouen, France, June 2018.
    https://hal.inria.fr/hal-01811454
  • 43C. Tallec, Y. Ollivier.
    Unbiased online recurrent optimization, in: International Conference On Learning Representation, Vancouver, Canada, April 2018.
    https://hal.archives-ouvertes.fr/hal-01972587

Scientific Books (or Scientific Book chapters)

  • 44O. Goudet, D. Kalainathan, P. Caillou, D. Lopez-Paz, I. Guyon, M. Sebag.
    Learning Functional Causal Models with Generative Neural Networks, in: Explainable and Interpretable Models in Computer Vision and Machine Learning, Springer Series on Challenges in Machine Learning, Springer International Publishing, 2018, https://arxiv.org/abs/1709.05321. [ DOI : 10.1007/978-3-319-98131-4 ]
    https://hal.archives-ouvertes.fr/hal-01649153
  • 45I. Guyon, L. Sun-Hosoya, M. Boullé, H. J. Escalante, S. Escalera, Z. Liu, D. Jajetic, B. Ray, M. Saeed, M. Sebag, A. Statnikov, W.-W. Tu, E. Viegas.
    Analysis of the AutoML Challenge series 2015-2018, in: AutoML: Methods, Systems, Challenges, F. Hutter, L. Kotthoff, J. Vanschoren (editors), The Springer Series on Challenges in Machine Learning, Springer Verlag, 2018.
    https://hal.archives-ouvertes.fr/hal-01906197

Books or Proceedings Editing

  • 46G. Bastin, P. Tubaro (editors)
    Big data, sociétés et sciences sociales, Presses de Sciences Po, September 2018, vol. 59, no 3.
    https://hal.archives-ouvertes.fr/hal-01885457
  • 47H. J. Escalante, S. Escalera, I. Guyon, X. Baró, Y. Güçlütürk, U. Güçlü, M. A. J. van Gerven (editors)
    Explainable and interpretable models in computer vision and machine learning, The Springer Series on Challenges in Machine Learning, Springer Verlag, January 2018. [ DOI : 10.1007/978-3-319-98131-4 ]
    https://hal.inria.fr/hal-01991623
  • 48E. Lutton, P. Legrand, P. Parrend, N. Monmarché, M. Schoenauer (editors)
    Artificial Evolution, 13th International Conference, Evolution Artificielle, EA 2017, Paris, France, October 25-27, 2017,Revised Selected Papers, Artificial Evolution, Springer, March 2018, vol. LNCS - Lecture Notes in Computer Science, no 10764. [ DOI : 10.1007/978-3-319-78133-4 ]
    https://hal.inria.fr/hal-01910443

Internal Reports

  • 49C. Furtlehner, J.-M. Lasgouttes, A. Attanasi, L. Meschini, M. Pezzulla.
    Spatio-temporal Probabilistic Short-term Forecasting on Urban Networks, Inria Saclay, équipe TAU ; Inria de Paris, équipe RITS ; PTV-SISTeMA, December 2018, no RR-9236.
    https://hal.inria.fr/hal-01964270

Scientific Popularization

  • 50C. Villani, M. Schoenauer, Y. Bonnet, C. Berthet, A.-C. Cornut, F. Levin, B. Rondepierre.
    Donner un sens à l'intelligence artificielle : Pour une stratégie nationale et européenne, Mission Villani sur l’intelligence artificielle, March 2018.
    https://hal.inria.fr/hal-01967551

Other Publications

  • 51M. A. Barry, V. Berthier, M.-C. Cambourieux, R. Pollès, B. D. Wilts, O. Teytaud, E. Centeno, N. Biais, A. Moreau.
    Evolutionary algorithms converge towards evolved biological photonic structures, August 2018, working paper or preprint.
    https://hal.archives-ouvertes.fr/hal-01856961
  • 52A. A. Casilli, P. Tubaro.
    Notre vie privée, un concept négociable, Le Monde, January 2018.
    https://hal.archives-ouvertes.fr/hal-01885452
  • 53B. Donnot.
    Learning To Run a Power Network Competition - Poster, December 2018, CiML Workshop, NeurIPS, Poster.
    https://hal.archives-ouvertes.fr/hal-01978748
  • 54B. Donnot, I. Guyon, Z. Liu, M. Schoenauer, A. Marot, P. Panciatici.
    Latent Surgical Interventions in Residual Neural Networks, October 2018, working paper or preprint.
    https://hal.archives-ouvertes.fr/hal-01906170
  • 55B. Donnot, I. Guyon, A. Marot, M. Schoenauer, P. Panciatici.
    Optimization of computational budget for power system risk assessment, May 2018, https://arxiv.org/abs/1805.01174 - working paper or preprint.
    https://hal.archives-ouvertes.fr/hal-01783685
  • 56H. J. Escalante, H. Kaya, A. A. Salah, S. Escalera, Y. Gucluturk, U. Güçlü, X. Baró, I. Guyon, J. J. Junior, M. Madadi, S. Ayache, E. Viegas, F. Gurpinar, A. S. Wicaksana, C. C. S. Liem, M. A. J. van Gerven, R. van Lier.
    Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos, January 2019, https://arxiv.org/abs/1802.00745 - Preprint submitted to IJCV.
    https://hal.inria.fr/hal-01991652
  • 57D. Kalainathan, O. Goudet, I. Guyon, D. Lopez-Paz, M. Sebag.
    SAM: Structural Agnostic Model, Causal Discovery and Penalized Adversarial Learning, August 2018, https://arxiv.org/abs/1803.04929 - working paper or preprint.
    https://hal.archives-ouvertes.fr/hal-01864239
  • 58J. Lehman, J. Clune, D. Misevic, C. Adami, J. Beaulieu, P. J. Bentley, S. Bernard, G. Beslon, D. M. Bryson, N. Cheney, A. Cully, S. Doncieux, F. C. Dyer, K. O. Ellefsen, R. Feldt, S. Fischer, S. Forrest, A. Frenoy, C. Gagneé, L. Le Goff, L. M. Grabowski, B. Hodjat, L. Keller, C. Knibbe, P. Krcah, R. E. Lenski, H. Lipson, R. MacCurdy, C. Maestre, R. Miikkulainen, S. Mitri, D. E. Moriarty, J.-B. Mouret, A. D. Nguyen, C. Ofria, M. Parizeau, D. Parsons, R. T. Pennock, W. F. Punch, T. S. Ray, M. Schoenauer, E. Shulte, K. Sims, K. O. Stanley, F. Taddei, D. Tarapore, S. Thibault, W. Weimer, R. Watson, J. Yosinksi.
    The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities, March 2018, https://arxiv.org/abs/1803.03453 - working paper or preprint.
    https://hal.inria.fr/hal-01735473
  • 59S. Liégeois, O. François, F. Jay.
    Dimension reduction adapted to paleogenomics, September 2018, Paris-Saclay Junior Conference on Data Science and Engineering, Poster.
    https://hal.archives-ouvertes.fr/hal-01978650
  • 60A. A. Pol, G. Cerminara, C. Germain, M. Pierini, A. Seth.
    Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider, January 2019, https://arxiv.org/abs/1808.00911 - working paper or preprint.
    https://hal.inria.fr/hal-01976256
  • 61D. Rousseau, S. Amrouche, P. Calafiura, V. Estrade, S. Farrell, C. Germain, V. Gligorov, T. Golling, H. Gray, I. Guyon, M. Hushchyn, V. Innocente, M. Kiehn, A. Salzburger, A. Ustyuzhanin, J.-R. V. Vlimant, Y. Yilmaz.
    The TrackML Particle Tracking Challenge, July 2018, The document describes the challenge data, task and organization.
    https://hal.inria.fr/hal-01680537
  • 62L. Sun-Hosoya, I. Guyon, M. Sebag.
    ActivMetaL: Algorithm Recommendation with Active Meta Learning, September 2018, IAL 2018 workshop, ECML PKDD, Poster.
    https://hal.archives-ouvertes.fr/hal-01931262
  • 63A. Zampieri, G. Charpiat, Y. Tarabalka.
    Coarse to fine non-rigid registration: a chain of scale-specific neural networks for multimodal image alignment with application to remote sensing, February 2018, https://arxiv.org/abs/1802.09816 - working paper or preprint.
    https://hal.inria.fr/hal-01718263
References in notes
  • 64I. Guyon et al. (editor)
    Cause Effect Pairs in Machine Learning, Springer, 2019, In preparation.
  • 65C. Adam-Bourdarios, G. Cowan, C. Germain, I. Guyon, B. Kégl, D. Rousseau.
    How Machine Learning won the Higgs Boson Challenge, in: Proc. European Symposium on ANN, CI and ML, 2016.
  • 66C. Adam-Bourdarios, G. Cowan, C. Germain-Renaud, I. Guyon, B. Kégl, D. Rousseau.
    The Higgs Machine Learning Challenge, in: Journal of Physics: Conference Series, 2015, vol. 664, no 7. [ DOI : 10.1088/1742-6596/664/7/072015 ]
    https://hal.inria.fr/hal-01745998
  • 67A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, S. Savarese.
    Social LSTM: Human Trajectory Prediction in Crowded Spaces, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 961–971.
  • 68D. J. Amit, H. Gutfreund, H. Sompolinsky.
    Statistical Mechanics of Neural Networks Near Saturation, in: Annals of Physics, 1987, vol. 173, pp. 30-67.
  • 69C. Anderson.
    The End of Theory: The Data Deluge Makes the Scientific Method Obsolete, 2008, Wired Magazine.
    https://www.wired.com/2008/06/pb-theory/
  • 70W. Banzhaf, P. Nordin, R. Keller, F. Francone.
    Genetic Programming — An Introduction On the Automatic Evolution of Computer Programs and Its Applications, Morgan Kaufmann, 1998.
  • 71N. Belkhir.
    Per Instance Algorithm Configuration for Continuous Black Box Optimization, Université Paris-Saclay, November 2017.
    https://hal.inria.fr/tel-01669527
  • 72N. Belkhir, J. Dréo, P. Savéant, M. Schoenauer.
    Per instance algorithm configuration of CMA-ES with limited budget, in: Proc. ACM-GECCO, 2017, pp. 681-688.
    https://hal.inria.fr/hal-01613753
  • 73I. Bello, B. Zoph, V. Vasudevan, Q. V. Le.
    Neural Optimizer Search with Reinforcement Learning, in: Proceedings of the 34th International Conference on Machine Learning, ICML, 2017, pp. 459–468.
    http://proceedings.mlr.press/v70/bello17a.html
  • 74S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, J. W. Vaughan.
    A theory of learning from different domains, in: Machine Learning, 2018, vol. 79, no 1, pp. 151–175.
    http://link.springer.com/10.1007/s10994-009-5152-4
  • 75J. Bergstra, R. Bardenet, Y. Bengio, B. Kégl.
    Algorithms for Hyper-Parameter Optimization, in: 25th Annual Conference on Neural Information Processing Systems (NIPS 2011), J. Shawe-Taylor et al. (editor), Advances in Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2011, vol. 24.
  • 76E. Bourdu, M.-M. Pérétié, M. Richer.
    La qualité de vie au travail : un levier de compétitivité, Presses des Mines, 2016.
  • 77J. Bruna, S. Mallat.
    Invariant Scattering Convolution Networks, in: IEEE Trans. Pattern Anal. Mach. Intell., 2013, vol. 35, no 8, pp. 1872–1886.
  • 78Y. Choi, M. Choi, M. Kim, J. Ha, S. Kim, J. Choo.
    StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation, in: CoRR, 2017, vol. abs/1711.09020.
    http://arxiv.org/abs/1711.09020
  • 79J.-J. Christophe, J. Decock, O. Teytaud.
    Direct model predictive control, in: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2014.
    https://hal.inria.fr/hal-00958192
  • 80R. Couillet, M. Debbah.
    Random matrix methods for wireless communications, Cambridge University Press, 2011.
  • 81David Silver et al..
    Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm, in: CoRR, 2017, vol. abs/1712.01815.
    http://arxiv.org/abs/1712.01815
  • 82M. DeMaria, M. Mainelli, L. K. Shay, J. A. Knaff, J. Kaplan.
    Further improvements to the statistical hurricane intensity prediction scheme (SHIPS), in: Weather and Forecasting, 2005, vol. 20, no 4, pp. 531–543.
  • 83A. Decelle, G. Fissore, C. Furtlehner.
    Spectral dynamics of learning in restricted Boltzmann machines, in: EPL (Europhysics Letters), 2017, vol. 119, no 6, 60001 p.
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