Project-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

  • 17B. Donon, B. Donnot, I. Guyon, Z. Liu, A. Marot, P. Panciatici, M. Schoenauer.
    LEAP Nets for System Identification and Application to Power Systems, in: Neurocomputing, 2019, To appear, forthcoming.
    https://hal.inria.fr/hal-02422708
  • 18S. Giffard-Roisin, M. Yang, G. Charpiat, C. K. Bonfanti, B. Kégl, C. Monteleoni.
    Tropical Cyclone Track Forecasting using Fused Deep Learning from Aligned Reanalysis Data, in: Frontiers in Big Data, January 2020, https://arxiv.org/abs/1910.10566, forthcoming.
    https://hal.archives-ouvertes.fr/hal-02329437
  • 19F. P. Landes, G. Biroli, O. Dauchot, A. Liu, D. Reichman.
    Attractive versus truncated repulsive supercooled liquids: The dynamics is encoded in the pair correlation function, in: Physical Review E , January 2020, vol. 101, no 1, https://arxiv.org/abs/1906.01103. [ DOI : 10.1103/PhysRevE.101.010602 ]
    https://hal.inria.fr/hal-02439295
  • 20E. Lippiello, G. Petrillo, F. P. Landes, A. Rosso.
    Fault Heterogeneity and the Connection between Aftershocks and Afterslip, in: Bulletin of the Seismological Society of America, April 2019, vol. 109, no 3, pp. 1156-1163, https://arxiv.org/abs/1812.05862. [ DOI : 10.1785/0120180244 ]
    https://hal.inria.fr/hal-02156407
  • 21A. 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, in: Computing and Software for Big Science, January 2019, vol. 3, no 1. [ DOI : 10.1007/s41781-018-0020-1 ]
    https://hal.inria.fr/hal-02427998
  • 22P. Tubaro, A. A. Casilli.
    Micro-work, artificial intelligence and the automotive industry, in: Journal of Industrial and Business Economics = Economia e politica industriale, June 2019, pp. 1-13. [ DOI : 10.1007/s40812-019-00121-1 ]
    https://hal.archives-ouvertes.fr/hal-02148979
  • 23P. Tubaro.
    La vie privée, un bien commun ?, in: Regards croisés sur l'économie, 2019, vol. 23, no 2, pp. 129-137. [ DOI : 10.3917/rce.023.0129 ]
    https://hal.archives-ouvertes.fr/hal-02196333
  • 24P. Tubaro.
    Les Learning Analytics vus par la sociologie, in: Distances et Médiations des Savoirs, December 2019, no 28.
    https://hal.archives-ouvertes.fr/hal-02418562
  • 25P. Tubaro.
    Whose results are these anyway? Reciprocity and the ethics of “giving back” after social network research, in: Social Networks, November 2019. [ DOI : 10.1016/j.socnet.2019.10.003 ]
    https://hal.archives-ouvertes.fr/hal-02360709

International Conferences with Proceedings

  • 26V. Berger, M. Sebag.
    From abstract items to latent spaces to observed data and back: Compositional Variational Auto-Encoder, in: ECML PKDD 2019 - European Conference on Machine learning and knowledge discovery in databases, Würzburg, Germany, September 2019, https://arxiv.org/abs/2001.07910.
    https://hal.inria.fr/hal-02431955
  • 27L. Blier, P. Wolinski, Y. Ollivier.
    Learning with Random Learning Rates, in: ECML PKDD 2019 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Würzburg, Germany, September 2019, https://arxiv.org/abs/1810.01322 - 16 pages, 4 figures, code available on GitHub.
    https://hal.archives-ouvertes.fr/hal-01888352
  • 28M. Chandorkar, C. Furtlehner, B. Poduval, E. Camporeale, M. Sebag.
    Dynamic Time Lag Regression: Predicting What and When, in: ICLR 2020 - 8th International Conference on Learning Representations, Addis Abeba, Ethiopia, April 2020.
    https://hal.inria.fr/hal-02422148
  • 29G. Charpiat, N. Girard, L. Felardos, Y. Tarabalka.
    Input Similarity from the Neural Network Perspective, in: NeurIPS 2019 - 33th Annual Conference on Neural Information Processing Systems, Vancouver, Canada, December 2019.
    https://hal.inria.fr/hal-02394647
  • 30B. Donnot, B. Donon, I. Guyon, Z. Liu, A. Marot, P. Panciatici, M. Schoenauer.
    LEAP nets for power grid perturbations, in: ESANN 2019 - 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, April 2019, https://arxiv.org/abs/1908.08314.
    https://hal.archives-ouvertes.fr/hal-02268886
  • 31B. Donon, B. Donnot, I. Guyon, A. Marot.
    Graph Neural Solver for Power Systems, in: IJCNN 2019 - International Joint Conference on Neural Networks, Budapest, Hungary, July 2019.
    https://hal.archives-ouvertes.fr/hal-02175989
  • 33N. Girard, G. Charpiat, Y. Tarabalka.
    Noisy Supervision for Correcting Misaligned Cadaster Maps Without Perfect Ground Truth Data, in: IGARSS 2019 - IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, July 2019, https://arxiv.org/abs/1903.06529.
    https://hal.inria.fr/hal-02065211
  • 34J. Girard-Satabin, G. Charpiat, Z. Chihani, M. Schoenauer.
    CAMUS: A Framework to Build Formal Specifications for Deep Perception Systems Using Simulators, in: ECAI 2020 - 24th European Conference on Artificial Intelligence, Santiago de Compostela, Spain, June 2020.
    https://hal.inria.fr/hal-02440520
  • 35A. Khalel, O. Tasar, G. Charpiat, Y. Tarabalka.
    Multi-Task Deep Learning for Satellite Image Pansharpening and Segmentation, in: IGARSS 2019 - IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, July 2019.
    https://hal.inria.fr/hal-02276549
  • 36A. Marot, A. Rosin, L. Crochepierre, B. Donnot, P. Pinson, L. Boudjeloud-Assala.
    Interpreting atypical conditions in systems with deep conditional Autoencoders: the case of electrical consumption, in: ECML PKDD 2019 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Wurzburg, Germany, September 2019.
    https://hal.archives-ouvertes.fr/hal-02266963
  • 37M. Nabhan, M. Schoenauer, Y. Tourbier, H. Hage.
    Optimizing coverage of simulated driving scenarios for the autonomous vehicle, in: IEEE ICCVE 2019 - International Conference on Connected Vehicles and Expo, Graz, Austria, November 2019.
    https://hal.inria.fr/hal-02433530
  • 38A. Pavao, D. Kalainathan, L. Sun-Hosoya, K. P. Bennett, I. Guyon.
    Design and Analysis of Experiments: A Challenge Approach in Teaching, in: NeurIPS 2019 - 33th Annual Conference on Neural Information Processing Systems, Vancouver, Canada, December 2019.
    https://hal.inria.fr/hal-02415639
  • 39A. A. Pol, V. Berger, G. Cerminara, C. Germain, M. Pierini.
    Anomaly Detection With Conditional Variational Autoencoders, in: ICMLA 2019 - 18th IEEE International Conference on Machine Learning and Applications, Boca Raton, United States, 18th International Conference on Machine Learning Applications, December 2019.
    https://hal.inria.fr/hal-02396279
  • 40H. Rakotoarison, M. Schoenauer, M. Sebag.
    Automated Machine Learning with Monte-Carlo Tree Search, in: IJCAI-19 - 28th International Joint Conference on Artificial Intelligence, Macau, China, International Joint Conferences on Artificial Intelligence Organization, August 2019, pp. 3296-3303. [ DOI : 10.24963/ijcai.2019/457 ]
    https://hal.inria.fr/hal-02300884
  • 41A. Schoenauer Sebag, L. Heinrich, M. Schoenauer, M. Sebag, L. Wu, S. Altschuler.
    Multi-Domain Adversarial Learning, in: ICLR 2019 - Seventh annual International Conference on Learning Representations, New Orleans, United States, Tara Sainath, May 2019.
    https://hal.inria.fr/hal-01968180
  • 42C. Tallec, L. Blier, Y. Ollivier.
    Making Deep Q-learning methods robust to time discretization, in: ICML 2019 - Thirty-sixth International Conference on Machine Learning, Long Beach, United States, PMLR, 2019, no 97, pp. 6096-6104, https://arxiv.org/abs/1901.09732.
    https://hal.inria.fr/hal-02435523
  • 43P. Tubaro.
    Emergent relational structures at a "sharing economy" festival, in: COMPLEX NETWORKS 2018 - The 7th International Conference on Complex Networks and Their Applications, Cambridge, United Kingdom, Springer, 2019, vol. 2, pp. 559-571.
    https://hal.archives-ouvertes.fr/hal-01947911
  • 44A. Yale, S. Dash, R. Dutta, I. Guyon, A. Pavao, K. P. Bennett.
    Privacy Preserving Synthetic Health Data, in: ESANN 2019 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, April 2019.
    https://hal.inria.fr/hal-02160496

Conferences without Proceedings

  • 45Z. Liu, I. Guyon, J. C. S. Jacques Junior, M. Madadi, S. Escalera, A. Pavao, H. J. Escalante, W.-W. Tu, Z. Xu, S. Treguer.
    AutoCV Challenge Design and Baseline Results, in: CAp 2019 - Conférence sur l'Apprentissage Automatique, Toulouse, France, July 2019.
    https://hal.archives-ouvertes.fr/hal-02265053

Scientific Books (or Scientific Book chapters)

  • 46S. Amrouche, L. Basara, P. Calafiura, V. Estrade, S. Farrell, D. Ferreira, L. Finnie, N. Finnie, C. Germain, V. V. Gligorov, T. Golling, S. Gorbunov, H. Gray, I. Guyon, M. Hushchyn, V. Innocente, M. Kiehn, E. Moyse, J.-F. Puget, Y. Reina, D. Rousseau, A. Salzburger, A. Ustyuzhanin, J.-R. Vlimant, J. S. Wind, T. Xylouris, Y. Yilmaz.
    The Tracking Machine Learning Challenge: Accuracy Phase, in: The NeurIPS '18 Competition, The Springer Series on Challenges in Machine Learning, Springer, November 2019, vol. 8, pp. 231-264. [ DOI : 10.1007/978-3-030-29135-8_9 ]
    https://hal.inria.fr/hal-02427989
  • 47O. Goudet, D. Kalainathan, M. Sebag, I. Guyon.
    Learning Bivariate Functional Causal Models, in: Cause Effect Pairs in Machine Learning, I. Guyon, A. Statnikov, B. B. Batu (editors), The Springer Series on Challenges in Machine Learning, Springer Verlag, October 2019, pp. 101-153. [ DOI : 10.1007/978-3-030-21810-2_3 ]
    https://hal.inria.fr/hal-02433201
  • 48I. Guyon, A. Statnikov, B. B. Batu.
    Cause Effect Pairs in Machine Learning, The Springer Series on Challenges in Machine Learning, Springer Verlag, 2019. [ DOI : 10.1007/978-3-030-21810-2 ]
    https://hal.inria.fr/hal-02433195
  • 49I. Guyon, O. Goudet, D. Kalainathan.
    Evaluation Methods of Cause-Effect Pairs, in: Cause Effect Pairs in Machine Learning, I. Guyon, A. Statnikov, B. B. Batu (editors), The Springer Series on Challenges in Machine Learning, Springer Verlag, October 2019, pp. 27-99. [ DOI : 10.1007/978-3-030-21810-2_2 ]
    https://hal.inria.fr/hal-02433198
  • 50I. Guyon, A. Statnikov.
    Results of the Cause-Effect Pair Challenge, in: Cause Effect Pairs in Machine Learning, I. Guyon, A. Statnikov, B. B. Batu (editors), The Springer Series on Challenges in Machine Learning. Springer, Springer Verlag, October 2019, pp. 237-256. [ DOI : 10.1007/978-3-030-21810-2_7 ]
    https://hal.inria.fr/hal-02433204
  • 51D. Kalainathan, O. Goudet, M. Sebag, I. Guyon.
    Discriminant Learning Machines, in: Cause Effect Pairs in Machine Learning, I. Guyon, A. Statnikov, B. B. Batu (editors), The Springer Series on Challenges in Machine Learning, Springer Verlag, October 2019, pp. 155-189. [ DOI : 10.1007/978-3-030-21810-2_4 ]
    https://hal.inria.fr/hal-02433203

Internal Reports

  • 52A. A. Casilli, P. Tubaro, C. Le Ludec, M. Coville, M. Besenval, T. Mouhtare, E. Wahal.
    Micro-work in France. Behind Automation, New Forms of Precarious Labour?, Projet de recherche DiPLab, May 2019.
    https://hal.archives-ouvertes.fr/hal-02139528

Other Publications

References in notes
  • 67C. 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.
  • 68C. 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
  • 69D. J. Amit, H. Gutfreund, H. Sompolinsky.
    Statistical Mechanics of Neural Networks Near Saturation, in: Annals of Physics, 1987, vol. 173, pp. 30-67.
  • 70C. Anderson.
    The End of Theory: The Data Deluge Makes the Scientific Method Obsolete, 2008, Wired Magazine.
    https://www.wired.com/2008/06/pb-theory/
  • 71N. Belkhir.
    Per Instance Algorithm Configuration for Continuous Black Box Optimization, Université Paris-Saclay, November 2017.
  • 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.
  • 73I. Bello, B. Zoph, V. Vasudevan, Q. V. Le.
    Neural Optimizer Search with Reinforcement Learning, in: 34th ICML, 2017, pp. 459–468.
  • 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. [ DOI : 10.1007/s10994-009-5152-4 ]
  • 75J. Bergstra, R. Bardenet, Y. Bengio, B. Kégl.
    Algorithms for Hyper-Parameter Optimization, in: NIPS 25, 2011, pp. 2546–2554.
  • 76J. Bruna, S. Mallat.
    Invariant Scattering Convolution Networks, in: IEEE Trans. Pattern Anal. Mach. Intell., 2013, vol. 35, no 8, pp. 1872–1886.
  • 77J.-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
  • 78R. Couillet, M. Debbah.
    Random matrix methods for wireless communications, Cambridge University Press, 2011.
  • 79David Rousseau et al..
    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
  • 80David Rousseau et al..
    The TrackML Particle Tracking Challenge, July 2018.
    https://hal.inria.fr/hal-01680537
  • 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.
  • 84A. Decelle, G. Fissore, C. Furtlehner.
    Thermodynamics of Restricted Boltzmann Machines and Related Learning Dynamics, in: J. Stat. Phys., 2018, vol. 172, pp. 1576-1608.
  • 85L. Dinh, J. Sohl-Dickstein, S. Bengio.
    Density estimation using Real NVP, in: Int. Conf. on Learning Representations (ICLR), 2017.
  • 86B. 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
  • 87B. 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
  • 88T. Duriez, S. L. Brunton, B. R. Noack.
    Machine Learning Control – Taming Nonlinear Dynamics and Turbulence, Springer International Publishing, 2017.
  • 89D. 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.
  • 90T. 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: J. Royal Statistical Society: B, 2012, vol. 74, no 3, pp. 361–417. [ DOI : 10.1111/j.1467-9868.2011.01025.x ]
  • 91H. 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
  • 92S. 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.
  • 93V. 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
  • 94S. Falkner, A. Klein, F. Hutter.
    BOHB: Robust and Efficient Hyperparameter Optimization at Scale, in: Proc. 35th ICML, 2018, pp. 1436–1445.
  • 95M. Feurer, A. Klein, K. Eggensperger, J. Springenberg, M. Blum, F. Hutter.
    Efficient and Robust Automated Machine Learning, in: NIPS 28, 2015, pp. 2962–2970.
  • 96L. Franceschi, P. Frasconi, S. Salzo, R. Grazzi, M. Pontil.
    Bilevel Programming for Hyperparameter Optimization and Meta-Learning, in: Proc. 35th ICML, J. G. Dy, A. Krause (editors), 2018, pp. 1563–1572.
  • 97C. 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.
  • 98C. Furtlehner, M. Sebag, X. Zhang.
    Scaling analysis of affinity propagation, in: Physical Review E, 2010, vol. 81, no 6, 066102 p.
  • 99Y. 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.
  • 100K. 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.
  • 101S. 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
  • 102J. Giles.
    Computational social science: Making the links, in: Nature - News, 2012, vol. 488, no 7412, pp. 448-450.
  • 103F. Gonard.
    Cold-start recommendation : from Algorithm Portfolios to Job Applicant Matching, Université Paris-Saclay, May 2018.
    https://tel.archives-ouvertes.fr/tel-01825220
  • 104F. 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 , Proceedings of Machine Learning Research, 2017, vol. 79, pp. 8-11.
  • 105I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio.
    Generative Adversarial Nets, in: NIPS 27, 2014, pp. 2672–2680.
  • 106I. 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.
  • 107O. 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. [ DOI : 10.1007/978-3-319-98131-4 ]
    https://hal.archives-ouvertes.fr/hal-01649153
  • 108P. Grunwald.
    The Minimum Description Length Principle, MIT Press, 2007.
  • 109I. Guyon, C. F. Aliferis, G. F. Cooper, A. Elisseeff, J.-P. Pellet, P. Spirtes, A. R. Statnikov.
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