MASCOT PhD student 2020 Meeting
Due to the cancellation of the MASCOT-NUM 2020 meeting, a special meeting devoted to the MASCOT-NUM PhD students' day is organized from September 17th to September 18th, 2020, in Grenoble Alpes University (France).
It will be located at the Auditorium of the IMAG building, Grenoble University.
This meeting is devoted to PhD students communications on topics related to the Mascot-Num network activities, i.e. sensitivity analysis, uncertainty quantification in simulation, design and modelling of computer experiments, model calibration and validation, structural reliability, optimization under uncertainty, data-driven modelling methods, etc.
For the poster session, PhD students are invited to submit a two-page abstract using the LaTeX abstract template to be held on the same day. The PDF file shall be sent by May 11th, 2020 (deadline extended!) to firstname.lastname@example.org
A prize of 1000€ will be awarded for the best oral presentation by the Mascot-Num scientific committee. A prize of 500€ will be awarded for the best poster by the Mascot-Num scientific committee.
All the speech will be in English.
The best student presentation award has been granted to Jérôme Stenger (EDF R&D/Université de Toulouse).
The best student poster award has been granted to Clément Gauchy (CEA/Ecole Polytechnique).
Program & Handouts
Day 1: September 17th
11h45 Welcome buffet
13h00-13h15 Welcome speech
13h15-14h15 Introductive lecture: Romain Couillet (CentraleSupelec; University ParisSaclay; IDEX GSTATS Chair & MIAI LargeDATA Chair, University Grenoble-Alpes) - Can random matrices change the future of machine learning? - couillet2020slides.pdf
14h15-14h45 Poster blitz 1: Antoine Ajenjo, Bruno Barracosa, Clément Bénard, Alexis Cousin, Jhouben Cuesta, Nestor Demeure, Clément Gauchy, Thierry Gonon
14h45-15h15 Coffee break
15h15-15h45 PhD 1: Jérôme Stenger (Univ. Toulouse, EDF R&D) - Optimal Uncertainty Quantification of a Risk Measurement - stenger2020.pdf - stenger2020slides.pdf
15h45-16h15 PhD 2: (visioconference) Deyu Ming (Univ. College London) - Integrated Emulators for Systems of Computer Models - ming2020.pdf - ming2020slides.pdf
16h15-16h45 Poster blitz 2: Adrien Hirvoas, Corentin Houpert, Baptiste Kerleguer, Henri Mermoz Kouye, Sébastien Petit, Alvaro Rollon, Martin Wieskotten
16h45-18h15 Poster session
Day 2: September 18th
9h30-10h00 PhD 3: Cécile Haberstich (Ecole Centrale Nantes, CEA/DAM) - Principal Component Analysis and boosted optimal weighted least-squares for training tree tensor networks - haberstich2020.pdf - haberstich2020slides.pdf
10h00-10h30 PhD 4: Maria-Belen Heredia (INRAE, Univ. Grenoble Alpes) - Aggregated Shapley effects from an acceptance-rejection sample: application to an avalanche model - heredia2020.pdf - heredia2020slides.pdf
10h30-11h PhD 5: (visioconference) Louise Kimpton (Univ. of Exeter) - Correlated Bernoulli Processes Using De Bruijn Graphs - kimpton2020.pdf - kimpton2020slides.pdf
11h-11h30 Coffee break
11h30-12h00 PhD 6: Thomas Bittar (Ecole des Ponts ParisTech, EDF R&D) - A Decomposition Method by Interaction Prediction for the Optimization of Maintenance Scheduling - bittar2020.pdf - bittar2020slides.pdf
12h00-12h30 PhD 7: Victor Trappler (Univ. Grenoble-Alpes) - Robust Calibration of numerical models based on relative regret - trappler2020.pdf - trappler2020slides.pdf
12h30-14h00 Buffet (+ meeting of the jury for the attribution of the prices)
14h00 Attribution of the prices
Speaker: Romain COUILLET (professor at CentraleSupélec, University ParisSaclay; IDEX GSTATS Chair & MIAI LargeDATA Chair, University Grenoble-Alpes)
Title: Can random matrices change the future of machine learning?
Abstract: Many standard machine learning algorithms and intuitions are known to misbehave, if not dramatically collapse, when operated on large dimensional data. In this talk, we will show that large dimensional statistics, and particularly random matrix theory, not only can elucidate this behavior but provides a new set of tools to understand and (sometimes drastically) improve machine learning algorithms. Besides, we will show that our various theoretical findings are provably applicable to very realistic and not-so-large dimensional data.
List of posters
- A. Ajenjo (FEMTO-ST, EDF R&D) - Quantitative comparison of several methodologies used to model epistemic uncertainty associated with aleatory uncertainty in the context of structural reliability - ajenjo2020.pdf
- B. Barracosa (CentraleSupélec, EDF R&D) - Bayesian methods for multi-objective simulation-based optimization - barracosa2020.pdf
- C. Benard (Sorbonne Univ., Safran Tech) - Interpretable Random Forests for Industrial Applications - benard2020.pdf
- A. Cousin (IFPEN) - Chance constraint optimization of a complex system - Application to the design of a floating offshore wind turbine - cousin2020.pdf
- Jhouben Cuesta Ramirez (CEA, Univ. Grenoble) - Bayesian optimization for mixed continuous and categorical variables: A latent variable approach - cuesta2020.pdf
- N. Demeure (ENS Paris-Saclay) - Uncertainty Quantification and Numerical Accuracy - demeure2020.pdf
- C. Gauchy (CEA, Ecole Polytechnique) - Active learning strategies for fragility curves estimation with guarantees - gauchy2020.pdf
- T. Gonon (Ecole Centrale de Lyon) - Sequential incrementation of the dimension in computer experiments - gonon2020.pdf
- A. Hirvoas (Univ. Grenoble Alpes, IFPEN) - Recursive Bayesian Filtering approaches for parameter estimation of a wind turbine numerical model - hirvoas2020.pdf
- C. Houpert (CEA/DAM, Ecole polytechnique) - Inverse problems for stochastic neutronics - houpert2020.pdf
- B. Kerleguer (CEA/DAM, Ecole polytechnique) - Gaussian process multifidelity surrogate models for time-dependent outputs - kerleguer2020.pdf
- H.M. Kouye (Univ. Paris-Saclay) - Analyse de sensibilité de modèles à sorties stochastiques et fonctionnelles - kouye2020.pdf
- S. Petit (CentraleSupelec, Safran Aircraft Engines) - Numerical study on various estimators of the parameters of a Matérn covariance function for kriging - petit2020.pdf
- A. Rollon (Univ. Grenoble Alpes, EDF R&D) - Statistical analysis of numerical simulations of accidental transients in Pressurized Water Reactors - rollon2020.pdf
- M. Wieskotten (CEA/DEN, Univ. d'Avignon) - Censored data in spatial statistics: Plug-In Method and Bayesian Method - wieskotten2020.pdf
- S. El Garroussi (CERFACS) - Mixture of polynomial chaos expansions for uncertainty propagation in two-dimensional hydraulic models for flood forecasting - elgarroussi2020.pdf
- A. Gautier (Idiap, Univ. of Bern) - Sample-based estimation of probability density fields: a spatial extension of the logistic Gaussian process - gautier2020.pdf
- P. Fonseca (ISEG Lisbon School of Economics and Management) - Bayes factors for multinomial goodness-of-fit testing - fonseca2020.pdf
- N. Lüthen (ETH Zürich) - Surrogating stochastic simulators using sparse polynomial chaos expansion and extended Karhunen-Loeve decomposition - luethen2020.pdf
- E. Maalouf (Univ. of Neuchâtel) - Approximate inverse problem solving with joint generative models and set estimation in latent space - maalouf2020.pdf
- I. Tavares (ISEG Lisbon School of Economics and Management) - Quantification of Model Discrepancy in a State-Space Model: An Example from Macroeconomics - tavares2020.pdf
- T.T. Tran (IFPEN) - An adapted derivative-free optimization method for the optimal design of turbine blades - tran2020.pdf
- C. Traveletti (Idiap, Univ. of Bern) - Fast, large scale Gaussian Process-based Bayesian inversion for set estimation in Geophysics - travelletti2020.pdf
- X. Zhu (ETH Zürich) - Emulation of stochastic simulators using latent polynomial chaos expansions - zhu2020.pdf