Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

• 1R. Allesiardo.
  Multi-armed bandits for non-stationary data streams, Université Paris-Saclay ; LRI - CNRS, University Paris-Sud, October 2016.
  https://hal.inria.fr/tel-01420663
• 2J. Bensadon.
  Applications of Information Theory to Machine Learning, Université Paris-Saclay, February 2016.
  https://tel.archives-ouvertes.fr/tel-01297163
• 3M.-L. Cauwet.
  Uncertainties in Optimization, Université Paris Sud - Orsay, September 2016.
  https://hal.archives-ouvertes.fr/tel-01422274

Articles in International Peer-Reviewed Journals

• 4A. Auger, N. Hansen.
  Linear Convergence of Comparison-based Step-size Adaptive Randomized Search via Stability of Markov Chains, in: SIAM Journal on Optimization, June 2016.
  https://hal.inria.fr/hal-00877160
• 5C. Boufenar, M. Batouche, M. Schoenauer.
  An Artificial Immune System for Offline Isolated Handwritten Arabic Character Recognition, in: Evolving Systems, 2016, pp. 1-17. [ DOI : 10.1007/s12530-016-9169-1 ]
  https://hal.inria.fr/hal-01394841
• 6I. Brigui-Chtioui, P. Caillou.
  Multidimensional Decision Model for Classifying Learners: the case of Massive Online Open Courses (MOOCs) , in: Journal of Decision Systems, January 2017. [ DOI : 10.1080/12460125.2017.1252235 ]
  https://hal.inria.fr/hal-01391011
• 7A. D'Angelo, L. Ryan, P. Tubaro.
  Visualization in mixed-methods research on social networks, in: Sociological Research Online, May 2016, vol. 21, n^o 2, 15 p. [ DOI : 10.5153/sro.3996 ]
  https://hal.archives-ouvertes.fr/hal-01348848
• 8A. Decelle, F. Ricci-Tersenghi.
  Solving the inverse Ising problem by mean-field methods in a clustered phase space with many states, in: Physical Review E , July 2016, 6 pages, 5 figures. [ DOI : 10.1103/PhysRevE.94.012112 ]
  https://hal.archives-ouvertes.fr/hal-01250824
• 9A. Decelle, F. Ricci-Tersenghi, P. Zhang.
  Data quality for the inverse Ising problem, in: Journal of Physics A: Mathematical and Theoretical, August 2016, vol. 49, n^o 38. [ DOI : 10.1088/1751-8113/49/38/384001 ]
  https://hal.archives-ouvertes.fr/hal-01250822
• 10S. Escalera, V. Athitsos, I. Guyon.
  Challenges in multimodal gesture recognition, in: Journal of Machine Learning Research, 2016, vol. 17, n^o 72, pp. 1-54.
  https://hal.archives-ouvertes.fr/hal-01381158
• 11C. Furtlehner, A. Decelle.
  Cycle-based Cluster Variational Method for Direct and Inverse Inference, in: Journal of Statistical Physics, August 2016, vol. 164, n^o 3, pp. 531–574.
  https://hal.inria.fr/hal-01214155
• 12J. Garcia-Rodriguez, I. Guyon, S. Escalera, A. Psarrou, A. Lewis, M. Cazorla.
  Editorial: special issue on computational intelligence for vision and robotics, in: Neural Computing and Applications, 2016, pp. 1–2. [ DOI : 10.1007/s00521-016-2330-8 ]
  https://hal.archives-ouvertes.fr/hal-01381150
• 13E. 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, September 2016.
  https://hal.inria.fr/hal-01369906
• 14V. Martin, J.-M. Lasgouttes, C. Furtlehner.
  Latent binary MRF for online reconstruction of large scale systems, in: Annals of Mathematics and Artificial Intelligence, 2016, vol. 77, n^o 1, pp. 123-154. [ DOI : 10.1007/s10472-015-9470-x ]
  https://hal.inria.fr/hal-01186220
• 15M. Mısır, M. Sebag.
  Alors: An algorithm recommender system, in: Artificial Intelligence, December 2016, Published on-line.
  https://hal.inria.fr/hal-01419874
• 16P. Tubaro, L. Ryan, A. D'Angelo.
  The Visual Sociogram in Qualitative and Mixed-Methods Research, in: Sociological Research Online, May 2016, vol. 21, n^o 2, 1 p. [ DOI : 10.5153/sro.3864 ]
  https://hal.archives-ouvertes.fr/hal-01348491

Invited Conferences

• 17C. Doerr, N. Bredeche, E. Alba, T. Bartz-Beielstein, D. Brockhoff, B. Doerr, G. A. Eiben, M. Epitropakis, C. Fonseca, A. Guerreiro, E. Haasdijk.
  Tutorials at PPSN 2016, in: Parallel Problem Solving from Nature – PPSN XIV, Edinburgh, United Kingdom, Lecture Notes in Computer Science, September 2016, vol. 9921, pp. 1012-1022.
  https://hal.archives-ouvertes.fr/hal-01363919

International Conferences with Proceedings

• 18C. Adam-Bourdarios, G. Cowan, C. Germain, I. Guyon, B. Kégl, D. Rousseau.
  How Machine Learning won the Higgs Boson Challenge, in: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, April 2016.
  https://hal.inria.fr/hal-01423097
• 19O. Ait Elhara, A. Auger, N. Hansen.
  Permuted Orthogonal Block-Diagonal Transformation Matrices for Large Scale Optimization Benchmarking, in: GECCO 2016, Denver, United States, T. Friedrich, F. Neumann (editors), Proceedings GECCO'16, ACM-Press, July 2016, pp. 189-196. [ DOI : 10.1145/2908812.2908937 ]
  https://hal.inria.fr/hal-01308566
• 20Y. Akimoto, N. Hansen.
  Online Model Selection for Restricted Covariance Matrix Adaptation, 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, pp. 3-13.
  https://hal.inria.fr/hal-01333840
• 21Y. Akimoto, N. Hansen.
  Projection-Based Restricted Covariance Matrix Adaptation for High Dimension, in: Genetic and Evolutionary Computation Conference 2016, Denver, United States, T. Friedrich, F. Neumann (editors), Proc. ACM-GECCO'16, July 2016, pp. 197-204. [ DOI : 10.1145/2908812.2908863 ]
  https://hal.inria.fr/hal-01306551
• 22S. Astete-Morales, M.-L. Cauwet, O. Teytaud.
  Analysis of Different Types of Regret in Continuous Noisy Optimization, in: Genetic and Evolutionary Computation Conference 2016, Denver, United States, T. Friedrich, F. Neumann (editors), Proc. ACM-GECCO'16, July 2016, pp. 205-212.
  https://hal.archives-ouvertes.fr/hal-01347814
• 23A. Atamna, A. Auger, N. Hansen.
  Analysis of Linear Convergence of a (1 + 1)-ES with Augmented Lagrangian Constraint Handling, in: Genetic and Evolutionary Computation Conference (GECCO), Denver, United States, T. Friedrich, F. Neumann (editors), Proc. ACM-GECCO'16, July 2016, pp. 213-220.
  https://hal.inria.fr/hal-01318807
• 24A. Atamna, A. Auger, N. Hansen.
  Augmented Lagrangian Constraint Handling for CMA-ES—Case of a Single Linear Constraint, in: Proceedings of the 14th International Conference on Parallel Problem Solving from Nature, Edinburgh, United Kingdom, September 2016, pp. 181 - 191. [ DOI : 10.1007/978-3-319-45823-6_17 ]
  https://hal.inria.fr/hal-01390386
• 25A. Auger, D. Brockhoff, N. Hansen, D. Tušar, T. Tušar, T. Wagner.
  Benchmarking MATLAB's gamultiobj (NSGA-II) on the Bi-objective BBOB-2016 Test Suite, in: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, Denver, CO, United States, July 2016, pp. 1233-1239. [ DOI : 10.1145/2908961.2931706 ]
  https://hal.inria.fr/hal-01435445
• 26A. Auger, D. Brockhoff, N. Hansen, D. Tušar, T. Tušar, T. Wagner.
  Benchmarking RM-MEDA on the Bi-objective BBOB-2016 Test Suite, in: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, Denver, CO, United States, July 2016, pp. 1241-1247. [ DOI : 10.1145/2908961.2931707 ]
  https://hal.inria.fr/hal-01435449
• 27A. Auger, D. Brockhoff, N. Hansen, D. Tušar, T. Tušar, T. Wagner.
  Benchmarking the Pure Random Search on the Bi-objective BBOB-2016 Testbed, in: Proceedings of the 2016 Genetic and Evolutionary Computation Conference Companion, Denver, CO, United States, July 2016, pp. 1217-1223. [ DOI : 10.1145/2908961.2931704 ]
  https://hal.inria.fr/hal-01435455
• 28A. Auger, D. Brockhoff, N. Hansen, D. Tušar, T. Tušar, T. Wagner.
  The Impact of Search Volume on the Performance of RANDOMSEARCH on the Bi-objective BBOB-2016 Test Suite, in: Proceedings of the 2016 Genetic and Evolutionary Computation Conference Companion, Denver, CO, United States, July 2016, pp. 1257 - 1264. [ DOI : 10.1145/2908961.2931709 ]
  https://hal.inria.fr/hal-01435453
• 29A. Auger, D. Brockhoff, N. Hansen, D. Tušar, T. Tušar, T. Wagner.
  The Impact of Variation Operators on the Performance of SMS-EMOA on the Bi-objective BBOB-2016 Test Suite, in: Proceedings of the 2016 Genetic and Evolutionary Computation Conference Companion (GECCO '16 Companion), Denver, CO, United States, July 2016, pp. 1225 - 1232. [ DOI : 10.1145/2908961.2931705 ]
  https://hal.inria.fr/hal-01435456
• 30N. Belkhir, J. Dréo, P. Savéant, M. Schoenauer.
  Feature Based Algorithm Configuration: A Case Study with Differential Evolution, in: Parallel Problem Solving from Nature – PPSN XIV, Edinburgh, France, J. Handl, E. Hart, P. Lewis, M. López-Ibáñez, G. Ochoa, B. Paechter (editors), LNCS, Springer Verlag, September 2016, vol. 9921, pp. 156-165. [ DOI : 10.1007/978-3-319-45823-6_15 ]
  https://hal.inria.fr/hal-01359539
• 31N. Belkhir, J. Dréo, P. Savéant, M. Schoenauer.
  Surrogate Assisted Feature Computation for Continuous Problems, in: LION 10 Learning and Intelligent OptimizatioN Conference, Ischia, Italy, P. Festa (editor), Proc. Learning and Intelligent OptimizatioN Conference, Springer Verlag, May 2016, forthcoming.
  https://hal.inria.fr/hal-01359543
• 32M.-L. Cauwet, O. Teytaud.
  Noisy Optimization: Fast Convergence Rates with Comparison-Based Algorithms, in: Genetic and Evolutionary Computation Conference, Denver, United States, T. Friedrich, F. Neumann (editors), Proc. ACM-GECCO'16, July 2016, pp. 1101-1106.
  https://hal.archives-ouvertes.fr/hal-01306636
• 33T. Cazenave, J. Liu, F. Teytaud, O. Teytaud.
  Learning opening books in partially observable games: using random seeds in Phantom Go, in: Computer intelligence and Games (CIG 2016), Santorini, Greece, Computer intelligence and Games, September 2016.
  https://hal.inria.fr/hal-01413229
• 34B. Chen, S. Escalera, I. Guyon, V. Ponce-Lopez, N. Shah, M. Oliu.
  Overcoming Calibration Problems in Pattern Labeling with Pairwise Ratings: Application to Personality Traits, in: European Conference on Computer Vision (ECCV 2016) Workshops, 2016.
  https://hal.archives-ouvertes.fr/hal-01381148
• 35A. Erraqabi, M. Valko, A. Carpentier, O.-A. Maillard.
  Pliable rejection sampling, in: International Conference on Machine Learning, New York City, United States, June 2016.
  https://hal.inria.fr/hal-01322168
• 36H. J. Escalante, V. Ponce-Lopez, J. Wan, M. A. Riegler, B. Chen, A. Clapes, S. Escalera, I. Guyon, X. Baro, P. Halvorsen, H. Müller, M. Larson.
  ChaLearn Joint Contest on Multimedia Challenges Beyond Visual Analysis: An overview, in: International Conference on Pattern Recognition (ICPR 2016) Workshops, 2016.
  https://hal.archives-ouvertes.fr/hal-01381144
• 37S. Escalera, M. Torres, B. Martinez, X. Baro, H. J. Escalante, I. Guyon, G. Tzimiropoulos, C. Corneou, M. Oliu, M. A. Bagheri, M. Valstar.
  ChaLearn Looking at People and Faces of the World: Face Analysis Workshop and Challenge 2016, in: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016.
  https://hal.archives-ouvertes.fr/hal-01381152
• 38E. Galván-López, E. Mezura-Montes, O. Ait ElHara, M. Schoenauer.
  On the Use of Semantics in Multi-objective Genetic Programming, in: 14th International Conference Parallel Problem Solving from Nature – PPSN XIV, Edinburgh, United Kingdom, J. Handl, E. Hart, P. R. Lewis, M. López-Ibáñez, G. Ochoa, B. Paechter (editors), LNCS - Lecture Notes in Computer Science, Springer Verlag, September 2016, vol. 9921, pp. 353 - 363. [ DOI : 10.1007/978-3-319-45823-6_33 ]
  https://hal.inria.fr/hal-01387632
• 39F. Gonard, M. Schoenauer, M. Sebag.
  Algorithm Selector and Prescheduler in the ICON challenge, in: International Conference on Metaheuristics and Nature Inspired Computing (META’2016), Marrakech, Morocco, October 2016.
  https://hal.inria.fr/hal-01378745
• 40G. Grefenstette, L. Muchemi.
  On the Place of Text Data in Lifelogs, and Text Analysis via Semantic Facets, in: iConference 2016 SIE on Lifelogging, Philadelphia, United States, March 2016.
  https://hal.inria.fr/hal-01328473
• 41G. Grefenstette, K. Rafes.
  Transforming Wikipedia into an Ontology-based Information Retrieval Search Engine for Local Experts using a Third-Party Taxonomy, in: Joint Second Workshop on Language and Ontology & Terminology and Knowledge Structures (LangOnto2 + TermiKS) LO2TKS, Portoroz, Slovenia, May 2016.
  https://hal.inria.fr/hal-01224114
• 42I. Guyon, I. Chaabane, H. J. Escalante, S. Escalera, D. Jajetic, J. R. Lloyd, N. Macia, B. Ray, L. Romaszko, M. Sebag, A. Statnikov, S. Treguer, E. Viegas.
  A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention, in: International Conference in Machine Learning (ICML 2016) Workshops, New-York, United States, ICML 2016 AutoML Workshop, JMLR, 2016, vol. 1, pp. 1-8.
  https://hal.archives-ouvertes.fr/hal-01381145
• 43A. O. Kazakçı, C. Mehdi, B. Kégl.
  Digits that are not: Generating new types through deep neural nets, in: International Conference on Computational Creativity, Paris, France, June 2016.
  https://hal.archives-ouvertes.fr/hal-01427556
• 44O. Krause, T. Glasmachers, N. Hansen, C. Igel.
  Unbounded Population MO-CMA-ES for the Bi-Objective BBOB Test Suite, in: GECCO'16 Companion of Proceedings of the 2016 Genetic and Evolutionary Computation Conference, Denver, United States, ACM, July 2016, pp. 1177-1184. [ DOI : 10.1145/2908961.2931699 ]
  https://hal.inria.fr/hal-01381653
• 45E. Maggiori, Y. Tarabalka, G. Charpiat, P. Alliez.
  Fully Convolutional Neural Networks For Remote Sensing Image Classification, in: IEEE International Geoscience and Remote Sensing Symposium, Beijing, China, Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, July 2016, pp. 5071-5074.
  https://hal.inria.fr/hal-01350706
• 46L. 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
• 47V. Ponce-Lopez, B. Chen, M. Oliu, C. Cornearu, A. Clapes, I. Guyon, X. Baro, H. J. Escalante, S. Escalera.
  ChaLearn LAP 2016: First Round Challenge on First Impressions - Dataset and Results, in: European Conference on Computer Vision (ECCV 2016) Workshops, Amsterdam, Netherlands, 2016. [ DOI : 10.1007/978-3-319-49409-8_32 ]
  https://hal.archives-ouvertes.fr/hal-01381149
• 48F. Popescu, S. Ayache, S. Escalera, X. Baró Solé, C. Capponi, P. Panciatici, I. Guyon.
  From geospatial observations of ocean currents to causal predictors of spatio-economic activity using computer vision and machine learning, in: EGU General Assembly Conference Abstracts, 2016.
  https://hal.archives-ouvertes.fr/hal-01381147
• 49T. Schmitt, P. Caillou, M. Sebag.
  Matching Jobs and Resumes: a Deep Collaborative Filtering Task, in: GCAI 2016 - 2nd Global Conference on Artificial Intelligence, Berlin, Germany, GCAI 2016. 2nd Global Conference on Artificial Intelligence, September 2016, vol. 41.
  https://hal.inria.fr/hal-01378589
• 50P. Taillandier, M. Bourgais, P. Caillou, C. Adam, B. Gaudou.
  A BDI agent architecture for the GAMA modeling and simulation platform, in: MABS 2016 Multi-Agent-Based Simulation, Singapore, Singapore, May 2016.
  https://hal.inria.fr/hal-01391002
• 51F. Teytaud, O. Teytaud.
  QR mutations improve many evolution strategies -a lot on highly multimodal problems, in: ACM-GECCO'16, Denver, United States, T. Friedrich, F. Neumann (editors), Poster in GECCO'16 Companion, July 2016, pp. 35-36. [ DOI : 10.1145/1235 ]
  https://hal.inria.fr/hal-01406727
• 52J. Wan, Y. Zhao, S. Zhou, I. Guyon, S. Escalera, S. Z. Li.
  ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture Recognition, in: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016.
  https://hal.archives-ouvertes.fr/hal-01381151

Conferences without Proceedings

• 53M.-L. Cauwet, O. Teytaud.
  Multivariate bias reduction in capacity expansion planning, in: 19th Power Systems Computation Conference, Gênes, Italy, June 2016.
  https://hal.archives-ouvertes.fr/hal-01306643
• 54M. Cherti, B. Kégl, A. O. Kazakçı.
  Out-of-class novelty generation: an experimental foundation *, in: Neural Information Processing Systems, Barcelona, Spain, December 2016.
  https://hal.archives-ouvertes.fr/hal-01427570
• 55G. Grefenstette, L. Muchemi.
  Determining the Characteristic Vocabulary for a Specialized Dictionary using Word2vec and a Directed Crawler, in: GLOBALEX 2016: Lexicographic Resources for Human Language Technology, Portoroz, Slovenia, May 2016.
  https://hal.inria.fr/hal-01323591
• 56D. 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

Scientific Books (or Scientific Book chapters)

• 57A. A. Casilli, P. Tubaro.
  Le phénomène "pro ana": Troubles alimentaires et réseaux sociaux, Presses des Mines, October 2016.
  https://hal.archives-ouvertes.fr/hal-01404890
• 58P. Tubaro.
  Formalization and mathematical modelling, in: History of Economic Analysis, H. K. Gilbert Faccarello (editor), Edward Elgar, July 2016, vol. III, pp. 208–221. [ DOI : 10.4337/9781785365065.00022 ]
  https://hal.archives-ouvertes.fr/hal-01350533

Books or Proceedings Editing

• 59S. Bonnevay, P. Legrand, N. Monmarché, E. Lutton, M. Schoenauer (editors)
  Artificial Evolution 2015, LNCS - Lecture Notes in Computer Science, Springer, Lyon, France, 2016, vol. 9554. [ DOI : 10.1007/978-3-319-31471-6 ]
  https://hal.inria.fr/hal-01389072
• 60A. D'Angelo, L. Ryan, P. Tubaro (editors)
  Peer Reviewed Special Section: Visualization in Mixed-Methods Research on Social NetworksSpecial section of Sociological Research Online, Guest edited by Alessio D'Angelo, Louise Ryan and Paola Tubaro, Sociological Research Online (special section), Sage, May 2016, vol. 21, n^o 2.
  https://hal.archives-ouvertes.fr/hal-01348492

Other Publications

• 61R. Allesiardo, R. Féraud, O.-A. Maillard.
  Random Shuffling and Resets for the Non-stationary Stochastic Bandit Problem, November 2016, working paper or preprint.
  https://hal.archives-ouvertes.fr/hal-01400320
• 62D. Brockhoff, T. Tusar, D. Tusar, T. Wagner, N. Hansen, A. Auger.
  Biobjective Performance Assessment with the COCO Platform, May 2016, ArXiv e-prints, arXiv:1605.01746.
  https://hal.inria.fr/hal-01315317
• 63A. Gopalan, O.-A. Maillard, M. Zaki.
  Low-rank Bandits with Latent Mixtures, November 2016, working paper or preprint.
  https://hal.archives-ouvertes.fr/hal-01400318
• 64N. Hansen, A. Auger, D. Brockhoff, D. Tusar, T. Tusar.
  COCO: Performance Assessment, May 2016, ArXiv e-prints, arXiv:1605.03560.
  https://hal.inria.fr/hal-01315318
• 65N. Hansen, A. Auger, O. Mersmann, T. Tusar, D. Brockhoff.
  COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting, July 2016, ArXiv e-prints, arXiv:1603.08785.
  https://hal.inria.fr/hal-01294124
• 66N. Hansen, T. Tusar, O. Mersmann, A. Auger, D. Brockhoff.
  COCO: The Experimental Procedure, May 2016, ArXiv e-prints, arXiv:1603.08776.
  https://hal.inria.fr/hal-01294167
• 67E. Maggiori, G. Charpiat, Y. Tarabalka, P. Alliez.
  Learning Iterative Processes with Recurrent Neural Networks to Correct Satellite Image Classification Maps, October 2016, working paper or preprint.
  https://hal.inria.fr/hal-01388551
• 68E. Maggiori, Y. Tarabalka, G. Charpiat, P. Alliez.
  High-Resolution Semantic Labeling with Convolutional Neural Networks, November 2016, working paper or preprint.
  https://hal.inria.fr/hal-01393279
• 69O.-A. Maillard.
  Self-normalization techniques for streaming confident regression, May 2016, working paper or preprint.
  https://hal.archives-ouvertes.fr/hal-01349727
• 70L. Marti, A. Fansi-Tchango, L. Navarro, M. Schoenauer.
  T. Friedrich, F. Neumann (editors), VorAIS: A Multi-Objective Voronoi Diagram-based Artificial Immune System, Poster at GECCO 2016 (Companion), July 2016, pp. 11 - 12, Genetic and Evolutionary Computation Conference, GECCO 2016, Poster. [ DOI : 10.1145/2908961.2909027 ]
  https://hal.inria.fr/hal-01400263
• 71L. Muchemi, G. Grefenstette.
  Word Embedding and Statistical Based Methods for Rapid Induction of Multiple Taxonomies, June 2016, working paper or preprint.
  https://hal.inria.fr/hal-01334236
• 72D. Rousseau, 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, S. Wenzel.
  The Tracking Machine Learning Challenge, December 2016, NIPS workshop: Challenges in Machine Learning (CiML), Poster.
  https://hal.inria.fr/hal-01422941
• 73S. Tfaili, D. Bui Thi, K. Rafes, A. Tfayli, A. Baillet-Guffroy, C. Germain, P. Chaminade.
  Data acquisition for analytical platforms: Automating scientific workflows and building an open database platform for chemical anlysis metadata, January 2016, Chimiométrie XVII, Poster.
  https://hal.inria.fr/hal-01423371
• 74T. Tusar, D. Brockhoff, N. Hansen, A. Auger.
  COCO: The Bi-objective Black Box Optimization Benchmarking (bbob-biobj) Test Suite, May 2016, ArXiv e-prints, arXiv:1604.00359.
  https://hal.inria.fr/hal-01296987