EN FR
EN FR


previous Previous | up Home


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.
  • 84L. Dinh, J. Sohl-Dickstein, S. Bengio.

    Density estimation using Real NVP, in: Int. Conf. on Learning Representations (ICLR), 2017.
  • 85T. Duriez, S. L. Brunton, B. R. Noack.

    Introduction, Springer International Publishing, 2017, pp. 1–10.
  • 86D. K. Duvenaud, D. Maclaurin, J. Aguilera-Iparraguirre, R. Gómez-Bombarelli, T. Hirzel, A. Aspuru-Guzik, R. P. Adams.

    Convolutional Networks on Graphs for Learning Molecular Fingerprints, in: NIPS, 2015, pp. 2224–2232.
  • 87M. Eickenberg, A. Gramfort, G. Varoquaux, B. Thirion.

    Seeing it all: Convolutional network layers map the function of the human visual system, in: NeuroImage, 2016. [ DOI : 10.1016/j.neuroimage.2016.10.001 ]

    https://hal.inria.fr/hal-01389809
  • 88T. v. Erven, P. Grunwald, S. de Rooij.

    Catching up faster by switching sooner: a predictive approach to adaptive estimation with an application to the AIC-BIC dilemma: Catching up Faster, in: Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2012, vol. 74, no 3, pp. 361–417.

    http://doi.wiley.com/10.1111/j.1467-9868.2011.01025.x
  • 89H. J. Escalante, I. Guyon, S. Escalera, J. Jacques, M. Madadi, X. Baró, S. Ayache, E. Viegas, Y. Güçlütürk, U. Güçlü, M. A. J. van Gerven, R. van Lier.

    Design of an Explainable Machine Learning Challenge for Video Interviews, in: IJCNN 2017 - 30th International Joint Conference on Neural Networks, Anchorage, AK, United States, Neural Networks (IJCNN), 2017 International Joint Conference on, IEEE, 2017, pp. 1-8.

    https://hal.inria.fr/hal-01668386
  • 90S. Escalera, X. Baró, H. J. Escalante, I. Guyon.

    ChaLearn looking at people: A review of events and resources, in: 2017 International Joint Conference on Neural Networks (IJCNN), 2017, pp. 1594-1601.
  • 91S. Falkner, A. Klein, F. Hutter.

    BOHB: Robust and Efficient Hyperparameter Optimization at Scale, in: Proceedings of the 35th International Conference on Machine Learning (ICML 2018), 2018, pp. 1436–1445.
  • 92M. Feurer, A. Klein, K. Eggensperger, J. Springenberg, M. Blum, F. Hutter.

    Efficient and Robust Automated Machine Learning, in: Advances in Neural Information Processing Systems 28, C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, R. Garnett (editors), Curran Associates, Inc., 2015, pp. 2962–2970.

    http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf
  • 93L. Franceschi, P. Frasconi, S. Salzo, R. Grazzi, M. Pontil.

    Bilevel Programming for Hyperparameter Optimization and Meta-Learning, in: Proc. 35th International Conference on Machine Learning, ICML, J. G. Dy, A. Krause (editors), JMLR Workshop and Conference Proceedings, JMLR.org, 2018, vol. 80, pp. 1563–1572.

    http://proceedings.mlr.press/v80/franceschi18a.html
  • 94C. Furtlehner, A. Decelle.

    Cycle-Based Cluster Variational Method for Direct and Inverse Inference, in: Journal of Statistical Physics, 2016, vol. 164, no 3, pp. 531–574.
  • 95C. Furtlehner, M. Sebag, X. Zhang.

    Scaling analysis of affinity propagation, in: Physical Review E, 2010, vol. 81, no 6, 066102 p.
  • 96M. Gabrié, A. Manoel, C. Luneau, J. Barbier, N. Macris, F. Krzakala, L. Zdeborová.

    Entropy and mutual information in models of deep neural networks, in: CoRR, 2018, vol. abs/1805.09785.

    http://arxiv.org/abs/1805.09785
  • 97Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, V. Lempitsky.

    Domain-Adversarial Training of Neural Networks, in: Journal of Machine Learning Research, 2016, vol. 17, no 59, pp. 1-35.
  • 98K. Garimella, G. D. F. Morales, A. Gionis, M. Mathioudakis.

    Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship, in: WWW, ACM, 2018, pp. 913–922.
  • 99J. Giles.

    Computational social science: Making the links, in: Nature - News, 2012, vol. 488, no 7412, pp. 448-450.
  • 100F. Gonard, M. Schoenauer, M. Sebag.

    ASAP.V2 and ASAP.V3: Sequential optimization of an Algorithm Selector and a Scheduler, in: Open Algorithm Selection Challenge 2017 , Brussels, Belgium, Proceedings of Machine Learning Research, September 2017, vol. 79, pp. 8-11.

    https://hal.inria.fr/hal-01659700
  • 101I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio.

    Generative Adversarial Nets, in: Advances in Neural Information Processing Systems 27, 2014, pp. 2672–2680.
  • 102I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio.

    Generative Adversarial Nets, in: NIPS 27, Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, K. Q. Weinberger (editors), Curran Associates, Inc., 2014, pp. 2672–2680.
  • 103P. Grunwald.

    The Minimum Description Length Principle, MIT Press, 2007.
  • 104I. Guyon, C. F. Aliferis, G. F. Cooper, A. Elisseeff, J.-P. Pellet, P. Spirtes, A. R. Statnikov.

    Design and Analysis of the Causation and Prediction Challenge, in: WCCI Causation and Prediction Challenge, JMLR W-CP, 2008, pp. 1–33.
  • 105I. Guyon, K. Bennett, G. Cawley, H. J. Escalante, S. Escalera, T. K. Ho, N. Macia, B. Ray, M. Saeed, A. Statnikov.

    Design of the 2015 ChaLearn AutoML challenge, in: Proc. IJCNN, IEEE, 2015, pp. 1–8.
  • 106I. Guyon, K. Bennett, G. Cawley, H. J. Escalante, S. Escalera, T. K. Ho, N. Macia, B. Ray, M. Saeed, A. Statnikov.

    Design of the 2015 ChaLearn AutoML challenge, in: Proc. IJCNN, IEEE, 2015, pp. 1–8.
  • 107I. Guyon, L. Bottou, B. Schölkopf, A. Statnikov, E. Viegas, J. M. Robins.

    NIPS workshop on causality, 2013.

    http://clopinet.com/isabelle/Projects/NIPS2013/
  • 108U. Güçlü, M. A. J. van Gerven.

    Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream, in: Journal of Neuroscience, 2015, vol. 35, no 27, pp. 10005–10014. [ DOI : 10.1523/JNEUROSCI.5023-14.2015 ]

    http://www.jneurosci.org/content/35/27/10005
  • 109D. Hernández-Lobato, P. Morales-Mombiela, D. Lopez-Paz, A. Suárez.

    Non-linear Causal Inference using Gaussianity Measures, in: Journal of Machine Learning Research, 2016, vol. 17, no 28, pp. 1-39.

    http://jmlr.org/papers/v17/14-375.html
  • 110H. H. Hoos.

    Programming by Optimization, in: Commun. ACM, 2012, vol. 55, no 2, pp. 70–80.
  • 111D. Hothersall.

    History of Psychology, McGraw-Hill, 2004.
  • 112G. W. Imbens, D. B. Rubin.

    Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction, Cambridge University Press, 2015.
  • 113D. Kalainathan, O. Goudet, P. Caillou, M. Sebag, P. Tubaro, E. Bourdu, T. Weil.

    Portraits de travailleurs, Presses des Mines, 2017.
  • 114D. Kalainathan, O. Goudet, P. Caillou, M. Sebag, P. Tubaro.

    Conditions objectives de travail et ressenti des individus : le rôle du management, 2017, 12 p, Synthèse n. 14 de ”La Fabrique de l'Industrie”.

    https://hal.inria.fr/hal-01742592
  • 115W. Kay, J. Carreira, K. Simonyan, B. Zhang, C. Hillier, S. Vijayanarasimhan, F. Viola, T. Green, T. Back, P. Natsev, M. Suleyman, A. Zisserman.

    The Kinetics Human Action Video Dataset, in: arXiv e-prints, May 2017, arXiv:1705.06950 p.
  • 116D. Kingma, M. Welling.

    Auto-encoding variational Bayes., in: Int. Conf. on Learning Representations (ICLR), 2014.
  • 117L. Kotthoff.

    Algorithm Selection for Combinatorial Search Problems: A Survey, in: Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach, C. Bessiere, L. De Raedt, L. Kotthoff, S. Nijssen, B. O'Sullivan, D. Pedreschi (editors), Lecture Notes in Computer Science, Springer, 2016, pp. 149–190.
  • 118A. Krizhevsky, I. Sutskever, G. E. Hinton.

    ImageNet Classification with Deep Convolutional Neural Networks, in: Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, NIPS'12, 2012, pp. 1097–1105.
  • 119D. Lazer, A. Pentland, L. Adamic, S. Aral, A. L. Barabasi, D. Brewer, N. Christakis, N. Contractor, J. Fowler, M. Gutmann, T. Jebara, G. King, M. Macy, D. Roy, M. Van Alstyne.

    Life in the network: the coming age of computational social science, in: Science, 2009, vol. 323, no 5915, pp. 721–723.
  • 120Y. LeCun.

    Deep Learning est mort. Vive Differentiable Programming!, 2018, Facebook Post.

    https://www.facebook.com/yann.lecun/posts/10155003011462143
  • 121J. Lehman, K. O. Stanley.

    Abandoning Objectives: Evolution Through the Search for Novelty Alone, in: Evol. Comput., 2011, vol. 19, no 2, pp. 189–223.

    http://dx.doi.org/10.1162/EVCO_a_00025
  • 122J. Z. Liang, E. Meyerson, R. Miikkulainen.

    Evolutionary architecture search for deep multitask networks, in: Proc. ACM-GECCO, H. E. Aguirre, K. Takadama (editors), ACM, 2018, pp. 466–473.

    http://doi.acm.org/10.1145/3205455.3205489
  • 123H. Lipson, J. B. Pollack.

    Automatic design and manufacture of robotic lifeforms, in: Nature Letters, 2000, vol. 406, pp. 974–978.
  • 124D. Lopez-Paz, K. Muandet, B. Schölkopf, I. O. Tolstikhin.

    Towards a Learning Theory of Cause-Effect Inference, in: Proceedings of the 32nd International Conference on Machine Learning, ICML, F. R. Bach, D. M. Blei (editors), 2015, pp. 1452–1461.

    http://jmlr.org/proceedings/papers/v37/lopez-paz15.html
  • 125C. Louizos, M. Reisser, T. Blankevoort, E. Gavves, M. Welling.

    Relaxed Quantization for Discretized Neural Networks, in: CoRR, 2018, vol. abs/1810.01875.

    http://arxiv.org/abs/1810.01875
  • 126C. Louizos, M. Reisser, T. Blankevoort, E. Gavves, M. Welling.

    Relaxed Quantization for Discretized Neural Networks, in: Int. Conf. on Learning Representations (ICLR), 2019.
  • 127E. Maggiori, Y. Tarabalka, G. Charpiat, P. Alliez.

    Can Semantic Labeling Methods Generalize to Any City? The Inria Aerial Image Labeling Benchmark, in: IEEE International Symposium on Geoscience and Remote Sensing (IGARSS), Fort Worth, United States, July 2017.

    https://hal.inria.fr/hal-01468452
  • 128L. Mescheder, A. Geiger, S. Nowozin.

    Which Training Methods for GANs do actually Converge?, in: International Conference on Machine Learning, 2018, pp. 3481–3490.

    http://proceedings.mlr.press/v80/mescheder18a.html
  • 129D. Molchanov, A. Ashukha, D. Vetrov.

    Variational Dropout Sparsifies Deep Neural Networks, in: ArXiv e-prints, January 2017.
  • 130M. 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
  • 131C. O'Neill.

    Weapons of Math Destruction, Crown Books, 2016.
  • 132J. Pearl.

    Causality: Models, Reasoning, and Inference (2nd edition), Cambridge University Press, 2009.
  • 133J. Pearl.

    Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution, in: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM, 2018, 3 p.

    https://doi.org/10.1145/3159652.3176182
  • 134G. Pleiss, M. Raghavan, F. Wu, J. M. Kleinberg, K. Q. Weinberger.

    On Fairness and Calibration, in: NIPS, 2017, pp. 5684–5693.
  • 135M. Raissi.

    Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations, in: JMLR, 2018, vol. 19, pp. 1–24.
  • 136S. Ravanbakhsh, J. Schneider, B. Póczos.

    Equivariance Through Parameter-Sharing, in: International Conference on Machine Learning, 2017, pp. 2892–2901.

    http://proceedings.mlr.press/v70/ravanbakhsh17a.html
  • 137E. Real, S. Moore, A. Selle, S. Saxena, Y. L. Suematsu, J. T. Q. Le, A. Kurakin.

    Large-Scale Evolution of Image Classifiers, in: Proc. ICML 2017, 2017.

    https://icml.cc/Conferences/2017/Schedule?type=Poster
  • 138J. R. Rice.

    The Algorithm Selection Problem, in: Advances in Computers, 1976, vol. 15, pp. 65 – 118. [ DOI : 10.1016/S0065-2458(08)60520-3 ]

    http://www.sciencedirect.com/science/article/pii/S0065245808605203
  • 139J. Rissanen.

    Information and Complexity in Statistical Modeling, Information Science and Statistics, Springer-Verlag, 2007.
  • 140P. Rolet, M. Sebag, O. Teytaud.

    Boosting Active Learning to Optimality: a Tractable Monte-Carlo, Billiard-based Algorithm, in: ECML, Bled, Slovenia, 2009, pp. 302-317.

    https://hal.inria.fr/inria-00433866
  • 141T. Sanchez, G. Charpiat, F. Jay.

    SPI-DNA: End-to-end Deep Learning Approach for Demographic History Inference, in: Paris-Saclay Junior Conference on Data Science and Engineering, Orsay, France, September 2017.

    https://hal.archives-ouvertes.fr/hal-01679385
  • 142M. Schmidt, H. Lipson.

    Distilling free-form natural laws from experimental data, in: Science, 2009, vol. 324, no 5923, pp. 81–85.
  • 143T. Schmitt, F. Gonard, P. Caillou, M. Sebag.

    Language Modelling for Collaborative Filtering: Application to Job Applicant Matching, in: 29th ICTAI, 2017, pp. 1-8.
  • 144S. Sigtia, E. Benetos, S. Dixon.

    An End-to-End Neural Network for Polyphonic Piano Music Transcription, in: IEEE/ACM Trans. Audio, Speech & Language Processing, 2016, vol. 24, no 5, pp. 927–939.
  • 145J. Sirignano, K. Spiliopoulos.

    DGM: A deep learning algorithm for solving partial differential equations, in: Journal of Computational Physics, 2018, vol. 375, pp. 1339 – 1364. [ DOI : 10.1016/j.jcp.2018.08.029 ]

    http://www.sciencedirect.com/science/article/pii/S0021999118305527
  • 146I. Stengers.

    Another Science Is Possible, Open Humanities Press, 2013.
  • 147D. H. Stern, R. Herbrich, T. Graepel.

    Matchbox: large scale online bayesian recommendations, in: WWW, ACM Press, 2010, 111 p. [ DOI : 10.1145/1526709.1526725 ]

    http://portal.acm.org/citation.cfm?doid=1526709.1526725
  • 148C. Sunstein, R. Thaler.

    Nudge: Improving Decisions about Health, Wealth, and Happiness, Yale University Press, 2008.
  • 149C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, R. Fergus.

    Intriguing properties of neural networks, in: CoRR, 2013, vol. abs/1312.6199.

    http://arxiv.org/abs/1312.6199
  • 150C. Tallec, Y. Ollivier.

    Can recurrent neural networks warp time?, in: International Conference on Learning Representations – ICLR, 2018.

    https://openreview.net/forum?id=SJcKhk-Ab
  • 151N. Tishby, N. Zaslavsky.

    Deep learning and the information bottleneck principle, in: 2015 IEEE Information Theory Workshop (ITW), 2015, pp. 1-5.
  • 152S. Triantafillou, I. Tsamardinos.

    Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets, in: Journal of Machine Learning Research, 2015, vol. 16, pp. 2147-2205.
  • 153J. Vanschoren, J. N. van Rijn, B. Bischl, L. Torgo.

    OpenML: networked science in machine learning, in: SIGKDD Explorations, 2013, vol. 15, no 2, pp. 49–60.

    https://arxiv.org/abs/1407.7722
  • 154Volodymyr Mnih et al..

    Human-level control through deep reinforcement learning, in: Nature, 2015, vol. 518, no 7540, pp. 529–533.

    https://doi.org/10.1038/nature14236
  • 155J. Wan, S. Escalera, G. Anbarjafari, H. J. Escalante, X. Baró, I. Guyon, M. Madadi, J. Allik, C. Lin, Y. Xie.

    Results and Analysis of ChaLearn LAP Multi-modal Isolated and Continuous Gesture Recognition, and Real versus Fake Expressed Emotions Challenges, in: International Conference on Computer Vision - ICCV 2017, 2017.

    https://hal.inria.fr/hal-01677974
  • 156M. Weimer, A. Karatzoglou, Q. Le, A. Smola.

    CoFiRank - Maximum Margin Matrix Factorization for Collaborative Ranking, in: Advances in Neural Information Processing Systems, 21st Annual Conference on Neural Information Processing Systems 2007, Vancouver, Canada, December 2007, pp. 222-230, http://books.nips.cc/papers/files/nips20/NIPS2007_0612.pdf.

    https://hal.archives-ouvertes.fr/hal-00482740
  • 157M. Welling.

    Intelligence per Kilowatthour, 2018, Keynote talk @ICML.

    https://icml.cc/Conferences/2018/Schedule?showEvent=1866
  • 158F. M. J. Willems, Y. M. Shtarkov, T. J. Tjalkens.

    The context-tree weighting method: basic properties, in: IEEE Transactions on Information Theory, 1995, vol. 41, no 3, pp. 653-664.
  • 159X. 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
  • 160M. de Bezenac, A. Pajot, P. Gallinari.

    Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge, in: arXiv preprint arXiv:1711.07970, 2017.
  • 161A. van den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, K. Kavukcuoglu.

    WaveNet: A Generative Model for Raw Audio, in: CoRR, 2016, vol. abs/1609.03499.

    http://arxiv.org/abs/1609.03499


previous Previous | up Home