Publications

Journal articles and main conference proceedings

[41] L. Tiao, V. Dutordoir, V. Picheny, "Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes", ICML 2023

[40] H. Moss, S. Ober, V. Picheny, Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation, AISTATS, 2023

[39] Y. Diouane, V. Picheny, R. Le Riche, A. Scotto Di Perrotolo, "TREGO: a Trust-Region Framework for Efficient Global Optimization", Journal of Global Optimization, 2022

[38] V. Picheny, H. Moss, L.Torossian, N. Durrande, "Bayesian Quantile and Expectile Optimization", UAI, 2022

[37] S. Vakili, H. Moss, A. Artemev, V. Dutordoir, V. Picheny, "Scalable Thompson Sampling using Sparse Gaussian Process Models", NeurIPS 2021

[36] Le Riche, R., Picheny, V. Revisiting Bayesian optimization in the light of the COCO benchmark. Struct Multidisc Optim (2021).

[35] J.L. Moore, A.E. Camaclang, A.L. Moore, C.E. Hauser, M.C. Runge, J. Morgan, K. McDougall, V. Picheny, L. Rumpff, "A framework for allocating conservation resources between multiple threats and actions", Conservation Biology, 1-11, 2021

[34] S Vakili, K Khezeli, V Picheny, "On Information Gain and Regret Bounds in Gaussian Process Bandits", AISTATS 2021

[33] M. Binois, V. Picheny, A. Habbal, "The Kalai-Smorodinsky solution for many-objective Bayesian optimization", JMLR, 21(150):1−42, 2020

[32] V. Picheny, V. Dutordoir, A. Artemev, N. Durrande, "Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation", ECML-PKDD 2020, arxiv version

[31] L.Torossian, V. Picheny, R. Faivre, A. Garivier, "A review on quantile regression for stochastic computer experiments", Reliability Engineering & System Safety, Volume 201, 2020, arxiv version

[30] D. Gaudrie, R. Le Riche, V. Picheny, B. Enaux, V. Herbert, "Modeling and optimization with Gaussian processes in reduced eigenbases" Structural and Multidisciplinary optimization, 2020, arxiv version

[29] F. Bachoc, C. Helbert, V. Picheny, "Gaussian process optimization with simulation failures: classification and convergence proof", Journal of Global Optimization (2020): 483-506.

[28] L.Torossian, A. Garivier, V. Picheny, "X-armed bandits: optimizing quantiles and other risks", ACML conference, 2019

[27] D. Gaudrie, R. le Riche, V. Picheny, B. Enaux, V. Herbert, "Targeting Solutions in Bayesian Multi-Objective Optimization: Sequential and Parallel Versions", Annals of Mathematics and Artificial Intelligence, Springer, 2019.

[26] J. Constantin, V. Picheny, J-E Bergez, "A method to assess the impact of soil available water capacity uncertainty on crop models with tipping-bucket approach", European Journal of Soil Science, 71 (3), 369-381, 2019

[25] V. Picheny, M. Binois, A. Habbal, "A Bayesian optimization approach to find Nash equilibria", Journal of Global Optimization, 73(1), 171-192, 2018, doi:10.1007/s10898-018-0688-0

[24] M. Binois, V. Picheny, "GPareto: An R Package for Gaussian-Process Based Multi-Objective Optimization and Analysis", Journal of Statistical Software, 89(8), 2019

[23] V. Picheny, R. Servien, N. Villa-Vialaneix, "Interpretable sparse SIR for functional data", Statistics and Computing, doi:10.1007/s11222-018-9806-6

[22] M. Champion, V. Picheny, M. Vignes, "Inferring large graphs using l1-penalized likelihood", Statistics and Computing, 28(4), pp. 905-921, 2017, doi:10.1007/s11222-017-9769-z

[21] V. Picheny, P. Casadebaig, R. Trépos, R. Faivre, D. Da Silva, P. Vincourt, E. Costes, "Using numerical plant models and phenotypic correlation space to design achievable ideotypes", Plant, Cell & Environment, DOI:10.1111/pce.13001

[20] T. Labopin-Richard, V. Picheny, "Sequential design of experiments for estimating quantiles of black-box functions", Statistica Sinica, 2017, doi:10.5705/ss.202016.0160

[19] V. Picheny, R. Trépos, P. Casadebaig, "Optimization of black-box models with uncertain climatic inputs - application to sunflower ideotype design", PLOS ONE, 2017

[18] H. Jalali, I. Van Nieuwenhuyse, V. Picheny, "Comparison of Kriging-based methods for simulation optimization with heterogeneous noise", European Journal of Operational Research (EJOR), 2017

[17] V. Picheny, R. Gramacy, S. Wild, S. Le Digabel, "Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian", Advances in on Neural Information Processing Systems (NIPS), 2016

[16] V. Picheny, D. Ginsbourger, Tipaluck Krityakierne, "Comment: Some Enhancements Over the Augmented Lagrangian Approach", Technometrics, Volume 58, Issue 1, pp.17-21, 2016

[15] V. Picheny, "Multiobjective optimization using Gaussian process emulators via stepwise uncertainty reduction", Statistics and Computing, Volume 25, Issue 6, pp. 1265-1280, 2015

[14] V. Picheny, "A Stepwise uncertainty reduction approach to constrained global optimization", JMLR W&CP 33 :787-795 (Proceedings of AISTATS 2014), 2014

[13] C. Chevalier, J. Bect, D. Ginsbourger, E. Vazquez, V. Picheny, Y. Richet, "Fast kriging-based stepwise uncertainty reduction with application to the identification of an excursion set", Technometrics, Volume 56, Issue 4, pp.455-465, 2014

[12] L. Buslig, J. Baccou, V. Picheny, "Construction and efficient implementation of adaptive objective-based design of experiments", Mathematical Geosciences, Volume 46(3), pp.285-313, 2014

[11] V. Picheny, D. Ginsbourger, "Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package", Computational Statistics and Data Analysis, Volume 71, pp.1035-1053, 2014

[10] C. Chevalier, V. Picheny, D. Ginsbourger, "KrigInv: An efficient and user-friendly implementation of batch-sequential inversion strategies based on kriging", Computational Statistics and Data Analysis, Volume 71, pp. 1021-1034, 2014

[9] V. Picheny, T. Wagner, D. Ginsbourger, "A benchmark of kriging-based infill criteria for noisy optimization", Structural and Multidisciplinary Optimization, Volume 48(3), pp.607-626, 2013

[8] Y. Richet, G. Caplin, J. Crevel, D. Ginsbourger, V. Picheny, "Using Efficient Global Optimization Algorithm to assist Nuclear Criticality Safety Assessment", Nuclear Science and Engineering, Volume 175(1), pp.1-18, 2013

[7] V. Picheny, D. Ginsbourger, "A non-stationary space-time Gaussian Process model for partially converged simulations", SIAM/ASA J. Uncertainty Quantification, Volume 1(1), pp. 57–78, 2013

[6] V. Picheny, D. Ginsbourger, Y. Richet, G. Caplin, "Quantile-based optimization of noisy computer experiments with tunable precision", Technometrics, Volume 55, Issue 1, 2013

[5] V. Picheny, D. Ginsbourger, Y. Richet, G. Caplin, "Rejoinder", Technometrics, Volume 55, Issue 1, 2013

[4] J. Bect, D. Ginsbourger, L. Li, V. Picheny, E. Vazquez, "Sequential design of computer experiments for the estimation of a probability of failure", Statistics and Computing, volume 22, pp. 773-793, 2012

[3] V. Picheny, D. Ginsbourger, O. Roustant, R.T. Haftka, “Adaptive designs of experiments for accurate approximation of a target region”, Journal of Mechanical Design, Volume 132, Issue 7, 2010

[2] F. Viana, V. Picheny, R.T. Haftka, “Using Cross Validation to Design Conservative Surrogates", AIAA Journal, Volume 48, Issue 10, pp. 2286-2298, 2010 (DOI: 10.2514/1.J050327).

[1] V. Picheny, N-H. Kim, R.T. Haftka, “Application of Bootstrap Method in Conservative Estimation of Reliability with Limited Samples”, Structural and Multidisciplinary Optimization, Volume 41, Issue 2, pp. 205-217, 2010

Preprints

V. Picheny, J. Berkeley, H. Moss, H. Stojic, U. Granta, S. Ober, A. Artemev, K. Ghani, A. Goodall, A. Paleyes, S. Vakili, S. Pascual-Diaz, S. Markou, J. Qing, N. Loka, I. Couckuyt, "Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow"

S. Vakili, V. Picheny, N. Durrande, "Regret Bounds for Noise-Free Bayesian Optimization"

V. Picheny, S. Vakili, A. Artemev, "Ordinal Bayesian Optimisation"

S. Mahevas, V. Picheny, JC Soulie, L. Rouan, P. Lambert, N. Dumoulin, S. Lehuta, R. Faivre, R. Le Riche, D. Brockhoff, H. Drouineau, "A practical guide for conducting calibration and decision-making optimisation with complex ecological models."

Selected conferences

D. Gaudrie, R. le Riche, V. Picheny, B. Enaux, V. Herbert, From CAD to Eigenshapes for Surrogate-based Optimization, Conference Paper, Proceedings of the 13th World Congress of Structural and Multidisciplinary Optimization, Beijing, China, May 20-24 2019.

Constantin, J., Lalu, R., Le Bas, C., Lacroix, B., Picheny, V. (2018). Impact of available water capacity uncertainty at the watershed scale on agronomic and hydrological variables. In: ESA 2018. Abstract book. Innovative cropping and farming systems for high quality food production systems. (p. 82-82). Presented at 15. ESA Congress, Genève, CHE (2018-08-27 - 2018-08-31)

D. Gaudrie, R. le Riche, V. Picheny, B. Enaux, V. Herbert, "Targeting Well-Balanced Solutions under a Restricted Budget", LION12, June 2018, Kalamata, Greece

M. Binois, V. Picheny, A. Habbal, "The Kalai-Smorodinski solution for many-objective Bayesian optimization", NIPS BayesOpt workshop, December 2017, Long Beach, USA, https://bayesopt.github.io/papers/2017/28.pdf

D. Gaudrie, R. le Riche, V. Picheny, B. Enaux, V. Herbert, "Targeting Well-Balanced Solutions in Multi-Objective Bayesian Optimization under a Restricted Budget", PGMO days, November 2017, Paris-Saclay, France

V. Picheny, M. Binois, A. Habbal, "Solving Kalai-Smorodinski Equilibria Using Gaussian Process Regression", ENBIS17, September 2017, Naples, Italy

V. Picheny, M. Binois, A. Habbal, "Bayesian Optimization Approaches to Compute Nash Equilibria", SIAM Conference on Optimization (OP17), May 2017, Vancouver, Canada

M. Binois, V. Picheny, "Pareto Fronts with Gaussian Process Conditional Simulations", SIAM Conference on Optimization (OP17), May 2017, Vancouver, Canada

R. Servien, V. Picheny, N. Villa-Vialaneix, (2016). "Interval sparsity for functional inverse regression". In 22nd International Conference on Computational Statistics (COMPSTAT), Satellite CRoNoS Workshop on Functional Data Analysis. Oviedo, Spain.

V. Picheny, R. Servien, N. Villa-Vialaneix, (2016). "Parcimonie par intervalle pour la régression inverse par tranche fonctionnelle". In 48e Journées de Statistique de la SFdS (JdS 2016). Montpellier, France.

P Casadebaig, B Poublan, R Trepos, V Picheny, P Debaeke, "Using Plant Phenotypic Plasticity to Improve Crop Performance and Stability Regarding Climatic Uncertainty: A Computational Study on Sunflower", Procedia Environmental Sciences 29, 142-143

V. Picheny, J. Vandel, M. Vignes, N. Villa-Vialaneix, "Reconstruction quality of a biological network when its constituting elements are partially observed", Proceedings of Seventeenth International Conference on Artificial Intelligence and Statistics (AI & Statistics 2014), Reykjavik, ISL

V. Picheny, "Optimisation par métamodélisation et réduction séquentielle d'incertitude", 42e congrès d'analyse numérique (CANUM), Carry-le-Rouet, France, April 2014

V. Picheny, "Space-time Gaussian processes for the approximation of partially converged simulations", 10th ENBIS-DEINDE 2011 Spring Conference, Torino, Italy, March 2011, preprint: http://hal-ecp.archives-ouvertes.fr/EC-PARIS/hal-00579876/fr/

L. Buslig, J. Baccou, V. Picheny, J. Liandrat, "Adaptive design of experiments: application to environmental safety studies", 10th ENBIS-DEINDE 2011 Spring Conference, Torino, Italy, March 2011

V. Picheny, D. Ginsbourger, Y. Richet, "Noisy Expected Improvement and on-line computation time allocation for the optimization of simulators with tunable fidelity", 2nd International Conference on engineering optimization, Lisbon, Portugal, September 2010

F. Viana, V. Picheny, and R.T. Haftka, "Conservative prediction via safety margin: design through cross validation and benefits of multiple surrogates", ASME International Design Engineering Technical Conferences \& Computers and Information in Engineering Conference (IDETC/ CIE), San Diego, USA, September 2009

F. Viana, V. Picheny, R.T. Haftka, “Safety Margins for Conservative Surrogates”, 8th World Congress on Structural and Multidisciplinary Optimization (WCSMO-8), Lisbon, Portugal, June 2009

R. Le Riche, V. Picheny, D. Ginsbourger, A. Meyer, N-H. Kim, “Gears design with shape uncertainties using Monte Carlo simulations and kriging”, 50th IAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Palm Springs, USA, May 2009

V. Picheny, D. Ginsbourger, O. Roustant, R.T. Haftka, “Adaptive designs of experiments for accurate approximation of target regions”, ENBIS8, Athens, September 2008

D. Ginsbourger, V. Picheny, O. Roustant, Y. Richet, “A new look at Kriging for the Approximation of Noisy Simulators with Tunable Fidelity”, 8th European Network for Business and Industrial Statistics conference (ENBIS8), Cd-Rom, Athens, September 2008

V. Picheny, D. Ginsbourger, O. Roustant, R.T. Haftka, “Design of experiments for constraint approximation”, communication at the MASCOT-NUM meeting in stochastic approaches for safety, CEA Cadarache, France, March 2008

V. Picheny, N-H. Kim, R.T. Haftka, “Conservative Predictions Using Surrogate Modeling”, 49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 2008, AIAA-2008-1716

V. Picheny, N-H. Kim, R.T. Haftka, “Conservative estimations of reliability with limited sampling”, ASME 2007 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2007), 2007

V. Picheny, N-H. Kim, R.T. Haftka and J. Peters, “Conservative Estimation of Probability of Failure”, 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2006, AIAA-2006-7038

V. Picheny, R.T. Haftka, “Using bootstrap methods for conservative estimations”, NSF Design, Service, and Manufacturing Grantees and Research Conference, St. Louis, Missouri, 2006

PhD Dissertation

My PhD directors were Raphael Haftka (University of Florida) and Alain Vautrin (Ecole des Mines de Saint Etienne) and I also was supervised by Nam-Ho Kim and Olivier Roustant. The manuscript is entitled "Improving accuracy and compensating for uncertainty in surrogate modeling".

manuscript: picheny_dissertation.pdf