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Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

Articles in International Peer-Reviewed Journals

  • 4K. CAYE, F. Jay, O. Michel, O. François.

    Fast Inference of Individual Admixture Coefficients Using Geographic Data, in: Annals Of Applied Statistics, 2018, forthcoming.

    https://hal.archives-ouvertes.fr/hal-01676712
  • 5A. Decelle, G. Fissore, C. Furtlehner.

    Spectral Dynamics of Learning Restricted Boltzmann Machines, in: EPL - Europhysics Letters, November 2017.

    https://hal.inria.fr/hal-01652314
  • 6C. Fernando Crispim-Junior, A. Gómez Uría, C. Strumia, M. Koperski, A. Konig, F. Negin, S. Cosar, A.-T. Nghiem, G. Charpiat, F. Bremond, D. P. Chau.

    Online recognition of daily activities by color-depth sensing and knowledge models, in: Sensors, June 2017, vol. 17, no 7, pp. 1-15. [ DOI : 10.3390/s17071528 ]

    https://hal.inria.fr/hal-01658438
  • 7I. Guyon, H. J. Escalante, V. Athitsos, P. Jangyodsuk, J. Wan.

    Principal motion components for one-shot gesture recognition, in: Pattern Analysis and Applications, February 2017, vol. 20, no 1, pp. 167 - 182. [ DOI : 10.1007/s10044-015-0481-3 ]

    https://hal.inria.fr/hal-01677941
  • 8Y. Güçlütürk, U. Güçlü, X. Baró, H. J. Escalante, I. Guyon, S. Escalera, M. A. J. van Gerven, R. van Lier.

    Multimodal First Impression Analysis with Deep Residual Networks, in: IEEE Transactions on Affective Computing, September 2017, vol. PP, no 99, pp. 1-14. [ DOI : 10.1109/TAFFC.2017.2751469 ]

    https://hal.inria.fr/hal-01668375
  • 9E. Maggiori, Y. Tarabalka, G. Charpiat, P. Alliez.

    High-Resolution Semantic Labeling with Convolutional Neural Networks, in: IEEE Transactions on Geoscience and Remote Sensing, December 2017, https://arxiv.org/abs/1611.01962.

    https://hal.inria.fr/hal-01393279
  • 10M. Mısır, M. Sebag.

    Alors: An algorithm recommender system, in: Artificial Intelligence, March 2017, vol. 244, pp. 291-314, Published on-line Dec. 2016.

    https://hal.inria.fr/hal-01419874
  • 11Y. 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
  • 12F. 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, 2017, Published online on 6 July 2017. [ DOI : 10.1080/10410236.2017.1339371 ]

    https://hal.archives-ouvertes.fr/hal-01520138
  • 13P. Tubaro.

    Les tensions entre sociologie et politique à l’aune d’une tentative de législation des sites web sur les troubles alimentaires , in: SociologieS, November 2017, pp. 1-13.

    https://hal.archives-ouvertes.fr/hal-01648305

Invited Conferences

  • 14F. Jay, S. S. Boitard, F. Austerlitz.

    Reconstructing past history from whole-genomes: an ABC approach handling recombining data, in: European Mathematical Genetics Meeting, Tartu, Estonia, April 2017.

    https://hal.archives-ouvertes.fr/hal-01679379

International Conferences with Proceedings

  • 15E. Agustsson, R. Timofte, S. Escalera, X. Baró, I. Guyon, R. Rothe.

    Apparent and real age estimation in still images with deep residual regressors on APPA-REAL database, in: FG 2017 - 12th IEEE International Conference on Automatic Face and Gesture Recognition, Washington DC, United States, May 2017, pp. 1-12.

    https://hal.inria.fr/hal-01677892
  • 16S. Amrouche, N. Braun, P. Calafiura, S. Farrell, J. Gemmler, C. Germain, V. Gligorov, T. Golling, H. Gray, I. Guyon, M. Hushchyn, V. Innocente, B. Kégl, S. Neuhaus, D. Rousseau, A. Salzburger, A. Ustyuzhanin, J.-R. Vlimant, C. Wessel, Y. Yilmaz.

    Track reconstruction at LHC as a collaborative data challenge use case with RAMP, in: Connecting The Dots / Intelligent Tracker, Orsay, France, March 2017, vol. 150, 00015 p. [ DOI : 10.1051/epjconf/201715000015 ]

    https://hal.archives-ouvertes.fr/hal-01584689
  • 17M. Asadi-Aghbolaghi, A. Clapes, M. Bellantonio, H. J. Escalante, V. Ponce-López, X. Baró, I. Guyon, S. Kasaei, S. Escalera.

    A Survey on Deep Learning Based Approaches for Action and Gesture Recognition in Image Sequences, in: FG 2017 - 12th IEEE Conference on Automatic Face and Gesture Recognition, Washington, DC, United States, IEEE, May 2017, pp. 476-483. [ DOI : 10.1109/FG.2017.150 ]

    https://hal.inria.fr/hal-01668383
  • 18N. Belkhir, J. DREO, P. Savéant, M. Schoenauer.

    Per instance algorithm configuration of CMA-ES with limited budget, in: GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference, Berlin, Germany, July 2017, pp. 681-688.

    https://hal.inria.fr/hal-01613753
  • 19I. Brigui-Chtioui, P. Caillou, E. Negre.

    Intelligent Digital Learning: Agent-Based Recommender System, in: ICMLC 2017 - 9th International Conference on Machine Learning and Computing, Singapore, Singapore, February 2017. [ DOI : 10.1145/3055635.3056592 ]

    https://hal.inria.fr/hal-01680527
  • 20B. Donnot, I. Guyon, M. Schoenauer, P. Panciatici, A. Marot.

    Introducing machine learning for power system operation support, in: IREP Symposium, Espinho, Portugal, August 2017, https://arxiv.org/abs/1709.09527.

    https://hal.inria.fr/hal-01581719
  • 21H. 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, May 2017, pp. 1-8. [ DOI : 10.1109/IJCNN.2017.7966320 ]

    https://hal.inria.fr/hal-01668386
  • 22E. Galván-López, L. Vázquez-Mendoza, M. Schoenauer, L. Trujillo.

    On the Use of Dynamic GP Fitness Cases in Static and Dynamic Optimisation Problems, in: EA 2017- International Conference on Artificial Evolution, Paris, France, Evelyne Lutton, October 2017, pp. 1-14.

    https://hal.inria.fr/hal-01648365
  • 23F. 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
  • 24Y. Güçlütürk, U. Güçlü, M. Perez, H. J. Escalante Balderas, X. Baró, I. Guyon, C. Andujar, J. J. Junior, M. Madadi, S. Escalera, M. A. J. Van Gerven, R. van Lier.

    Visualizing Apparent Personality Analysis with Deep Residual Networks, in: International Conference on Computer Vision - ICCV 2017, Venice, Italy, October 2017.

    https://hal.inria.fr/hal-01677962
  • 25E. 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
  • 26E. Maggiori, Y. Tarabalka, G. Charpiat, P. Alliez.

    Polygonization of Remote Sensing Classification Maps by Mesh Approximation, in: ICIP 2017 - IEEE International Conference on Image Processing, Beijing, China, September 2017, 5 p.

    https://hal.inria.fr/hal-01530460
  • 27L. Martí, A. Fansi-Tchango, L. Navarro, M. Schoenauer.

    Progressively Adding Objectives: A Case Study in Anomaly Detection, in: Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin, Germany, July 2017. [ DOI : 10.1145/3071178.3071333 ]

    https://hal.inria.fr/hal-01525611
  • 29T. Schmitt, F. Gonard, P. Caillou, M. Sebag.

    Language Modelling for Collaborative Filtering: Application to Job Applicant Matching, in: ICTAI 2017 - 29th IEEE International Conference on Tools with Artificial Intelligence, Boston, United States, November 2017, pp. 1-8.

    https://hal.inria.fr/hal-01659543
  • 30J. 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, Venice, Italy, October 2017.

    https://hal.inria.fr/hal-01677974
  • 31N. Yoshikawa, N. Belkhir, S. Suzuki.

    Recurrent Neural Network-based Fault Detector for Aileron Failures of Aircraft, in: ASCC 2017 - The 2017 Asian Control Conference, Gold Coast, Australia, December 2017.

    https://hal.inria.fr/hal-01669540

Conferences without Proceedings

  • 32V. Estrade, C. Germain, I. Guyon, D. Rousseau.

    Adversarial learning to eliminate systematic errors: a case study in High Energy Physics, in: NIPS 2017 - workshop Deep Learning for Physical Sciences, Long Beach, United States, December 2017, pp. 1-5.

    https://hal.inria.fr/hal-01665925
  • 33T. 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

Scientific Books (or Scientific Book chapters)

  • 34M. Asadi-Aghbolaghi, A. Clapes, M. Bellantonio, H. J. Escalante, V. Ponce-López, X. Baró, I. Guyon, S. Kasaei, S. Escalera.

    Deep learning for action and gesture recognition in image sequences: a survey , in: Gesture Recognition, Springer Verlag, July 2017, pp. 539-578, A reduced version of this paper appeared appeared in the Proceedings of 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017), 2017.

    https://hal.inria.fr/hal-01678006
  • 35F. Gonard, M. Schoenauer, M. Sebag.

    Algorithm Selector and Prescheduler in the ICON challenge, in: Bioinspired heuristic optimization, E.-G. Talbi, A. Nakib (editors), Computational Intelligence, Springer Verlag, 2017, forthcoming.

    https://hal.inria.fr/hal-01663174
  • 36D. Kalainathan, O. Goudet, P. Caillou, P. Tubaro, T. Weil, E. Bourdu.

    Portraits de travailleurs : Comprendre la qualité de vie au travail, Presses des Mines - La Fabrique de l'Industrie, 2017.

    https://hal.inria.fr/hal-01665423

Internal Reports

  • 37A. Decelle, G. Fissore, C. Furtlehner.

    Thermodynamics of Restricted Boltzmann Machines and Related Learning Dynamics, Inria Saclay Ile de France ; LRI, Université Paris-Sud, January 2018, no RR-9139, pp. 1-36.

    https://hal.inria.fr/hal-01675310

Scientific Popularization

Other Publications

References in notes
  • 54S. Escalera, I. Guyon, V. Athitsos (editors)

    Gesture Recognition, Springer, 2017.

    https://doi.org/10.1007/978-3-319-57021-1
  • 55C. 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.
  • 56R. Akrour, M. Schoenauer, M. Sebag.

    Programming by Feedback, in: International Conference on Machine Learning, JMLR Proceedings, JMLR.org, 2014, no 32, pp. 1503-1511.
  • 57M. Amil, N. Bredèche, C. Gagné, S. Gelly, M. Schoenauer, O. Teytaud.

    A Statistical Learning Perspective of Genetic Programming, in: EuroGP, Proceedings of EuroGP 09, Springer Verlag, 2009.
  • 58M. Andrychowicz, M. Denil, S. Gómez, M. W. Hoffman, D. Pfau, T. Schaul, N. de Freitas.

    Learning to learn by gradient descent by gradient descent, in: Advances in Neural Information Processing Systems 29, D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, R. Garnett (editors), Curran Associates, Inc., 2016, pp. 3981–3989.
  • 59J. 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, R. Zemel, P. Bartlett, F. Pereira, K. Weinberger (editors), Advances in Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2011, vol. 24.
  • 60S. Boitard, W. Rodríguez, F. Jay, S. Mona, F. Austerlitz.

    Inferring population size history from large samples of genome-wide molecular data-an approximate Bayesian computation approach, in: PLoS genetics, 2016, vol. 12, no 3, e1005877 p.
  • 61P. Brazdil, C. Giraud Carrier, C. Soares, R. Vilalta.

    Metalearning: Applications to Data Mining, Springer Verlag, 2009.
  • 62E. Brochu, V. M. Cora, N. De Freitas.

    A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning, in: arXiv preprint arXiv:1012.2599, 2010.
  • 63S. Brunton, J. Proctor, N. Kutz.

    Sparse Identification of Nonlinear Dynamics (SINDy), in: Bulletin of the American Physical Society, 2016, vol. 61.
  • 64T. Domhan, J. T. Springenberg, F. Hutter.

    Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves, in: Proceedings of the 24th International Conference on Artificial Intelligence, IJCAI'15, AAAI Press, 2015, pp. 3460–3468.

    http://dl.acm.org/citation.cfm?id=2832581.2832731
  • 65B. Donnot, I. Guyon, M. Schoenauer, A. Marot, P. Panciatici.

    Fast Power system security analysis with Guided Dropout, 2018, submitted.
  • 66C. 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.
  • 67Y. 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.
  • 68I. 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.
  • 69I. 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.
  • 70I. 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.
  • 71H. H. Hoos.

    Programming by Optimization, in: Commun. ACM, 2012, vol. 55, no 2, pp. 70–80.
  • 72Y. LeCun, Y. Bengio, G. Hinton.

    Deep learning, in: Nature, 2015, vol. 521, no 7553, pp. 436–444.
  • 73E. Maggiori.

    Learning approaches for large-scale remote sensing image classification, Université Côte d'Azur, June 2017.

    https://hal.inria.fr/tel-01589661
  • 74L. Marti, A. Fansi-Tchango, L. Navarro, M. Schoenauer.

    Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm, in: Parallel Problem Solving from Nature – PPSN XIV, Edinburgh, United Kingdom, J. Handl, E. Hart, P. Lewis, M. López-Ibáñez, G. Ochoa, B. Paechter (editors), LNCS, Springer Verlag, September 2016, vol. 9921, pp. 697-706. [ DOI : 10.1007/978-3-319-45823-6_65 ]

    https://hal.inria.fr/hal-01387621
  • 75J. M. Mooij, J. Peters, D. Janzing, J. Zscheischler, B. Schölkopf.

    Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks, in: Journal of Machine Learning Research, 2016, vol. 17, no 32, pp. 1-102.
  • 76Y. Ollivier.

    Riemannian metrics for neural networks I: Feedforward networks, in: Information and Inference, 2015, vol. 4, no 2, pp. 108–153.
  • 77Y. Ollivier.

    Riemannian metrics for neural networks II: Recurrent networks and learning symbolic data sequences, in: Information and Inference, 2015, vol. 4, no 2, pp. 154–193.
  • 78J. Pearl.

    Causality: Models, Reasoning, and Inference (2nd edition), Cambridge University Press, 2009.
  • 79D. Rousseau, P. Calafiura, C. Germain, V. Innocente, R. Cenci, M. Kagan, I. Guyon, D. Clark, S. Farrel, R. Carney, A. Salzburger, D. Costanzo, M. Elsing, T. Golling, T. Tong, J.-R. V. Vlimant.

    TrackML: a LHC Tracking Machine Learning Challenge, in: International Conference on Computing in High Energy and Nuclear Physics, San Francisco, United States, October 2016.

    https://hal.inria.fr/hal-01422939
  • 80I. Shpitser, K. Mohan, J. Pearl.

    Missing Data as a Causal and Probabilistic Problem, in: Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, UAI 2015, July 12-16, 2015, Amsterdam, The Netherlands, M. Meila, T. Heskes (editors), AUAI Press, 2015, pp. 802–811.

    http://auai.org/uai2015/proceedings/papers/204.pdf
  • 81D. H. Stern, H. Samulowitz, R. Herbrich, T. Graepel, L. Pulina, A. Tacchella.

    Collaborative Expert Portfolio Management, in: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, M. Fox, D. Poole (editors), AAAI Press, 2010.
  • 82C. Thornton, F. Hutter, H. H. Hoos, K. Leyton-Brown.

    Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms, in: Proc. of KDD-2013, 2013, pp. 847-855.
  • 83S. 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.
  • 84M. Yagoubi, M. Schoenauer.

    Asynchronous Master/Slave MOEAs and Heterogeneous Evaluation Costs, in: Genetic and Evolutionary Computation Conference (GECCO 2012), United States, T. Soule, J. H. Moore (editors), ACM Press, July 2012, pp. 1007-1014.

    https://hal.archives-ouvertes.fr/hal-00689965
  • 85M. Yagoubi.

    Multi-objective parallel evolutionary algorithms : Application to Diesel Combustion, Université Paris Sud - Paris XI, July 2012.

    https://tel.archives-ouvertes.fr/tel-00734108


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