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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.

    Design and Analysis of the Causation and Prediction Challenge, in: WCCI Causation and Prediction Challenge, JMLR W-CP, 2008, pp. 1–33.
  • 110I. Guyon, K. Bennett, G. Cawley, H. J. Escalante, S. Escalera, T. K. Ho, N. Macia, B. Ray, M. Saeed, A. Statnikov.

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