Members
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
XML PDF e-pub
PDF e-Pub


Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

[1]
R. 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
[2]
J. Bensadon.
Applications of Information Theory to Machine Learning, Université Paris-Saclay, February 2016.
https://tel.archives-ouvertes.fr/tel-01297163
[3]
M.-L. Cauwet.
Uncertainties in Optimization, Université Paris Sud - Orsay, September 2016.
https://hal.archives-ouvertes.fr/tel-01422274

Articles in International Peer-Reviewed Journals

[4]
A. 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
[5]
C. 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
[6]
I. 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
[7]
A. D'Angelo, L. Ryan, P. Tubaro.
Visualization in mixed-methods research on social networks, in: Sociological Research Online, May 2016, vol. 21, no 2, 15 p. [ DOI : 10.5153/sro.3996 ]
https://hal.archives-ouvertes.fr/hal-01348848
[8]
A. 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
[9]
A. 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, no 38. [ DOI : 10.1088/1751-8113/49/38/384001 ]
https://hal.archives-ouvertes.fr/hal-01250822
[10]
S. Escalera, V. Athitsos, I. Guyon.
Challenges in multimodal gesture recognition, in: Journal of Machine Learning Research, 2016, vol. 17, no 72, pp. 1-54.
https://hal.archives-ouvertes.fr/hal-01381158
[11]
C. 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
[12]
J. 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
[13]
E. 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
[14]
V. 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, no 1, pp. 123-154. [ DOI : 10.1007/s10472-015-9470-x ]
https://hal.inria.fr/hal-01186220
[15]
M. Mısır, M. Sebag.
Alors: An algorithm recommender system, in: Artificial Intelligence, December 2016, Published on-line.
https://hal.inria.fr/hal-01419874
[16]
P. Tubaro, L. Ryan, A. D'Angelo.
The Visual Sociogram in Qualitative and Mixed-Methods Research, in: Sociological Research Online, May 2016, vol. 21, no 2, 1 p. [ DOI : 10.5153/sro.3864 ]
https://hal.archives-ouvertes.fr/hal-01348491

Invited Conferences

[17]
C. 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

[18]
C. 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
[19]
O. 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
[20]
Y. 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
[21]
Y. 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
[22]
S. 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
[23]
A. 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
[24]
A. 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
[25]
A. 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
[26]
A. 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
[27]
A. 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
[28]
A. 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
[29]
A. 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
[30]
N. 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
[31]
N. 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
[32]
M.-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
[33]
T. 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
[34]
B. 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
[35]
A. 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
[36]
H. 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
[37]
S. 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
[38]
E. 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
[39]
F. 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
[40]
G. 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
[41]
G. 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
[42]
I. 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
[43]
A. 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
[44]
O. 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
[45]
E. 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
[46]
L. 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
[47]
V. 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
[48]
F. 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
[49]
T. 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
[50]
P. 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
[51]
F. 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
[52]
J. 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

[53]
M.-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
[54]
M. 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
[55]
G. 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
[56]
D. 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)

[57]
A. 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
[58]
P. 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

[59]
S. 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
[60]
A. 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, no 2.
https://hal.archives-ouvertes.fr/hal-01348492

Other Publications

[61]
R. 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
[62]
D. 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
[63]
A. 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
[64]
N. 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
[65]
N. 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
[66]
N. 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
[67]
E. 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
[68]
E. 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
[69]
O.-A. Maillard.
Self-normalization techniques for streaming confident regression, May 2016, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-01349727
[70]
L. 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
[71]
L. 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
[72]
D. 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
[73]
S. 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
[74]
T. 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