{"id":72,"date":"2023-09-12T15:03:43","date_gmt":"2023-09-12T13:03:43","guid":{"rendered":"https:\/\/project.inria.fr\/mascotnum2024\/?page_id=72"},"modified":"2024-06-13T10:56:21","modified_gmt":"2024-06-13T08:56:21","slug":"program","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/mascotnum2024\/program\/","title":{"rendered":"Program"},"content":{"rendered":"\n
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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Tuesday, April 2<\/strong><\/td>\n<\/tr>\n
\u00a0<\/td>\n17:00<\/td>\nRoom check-in<\/td>\n<\/tr>\n
\u00a0<\/td>\n18:00<\/td>\nConference registration<\/td>\n<\/tr>\n
\u00a0<\/td>\n19:45<\/td>\nDinner<\/td>\n<\/tr>\n
\u00a0<\/strong><\/td>\n<\/tr>\n
Wednesday, April 3<\/strong><\/td>\n<\/tr>\n
\u00a0<\/td>\n8:30<\/td>\nOpening<\/td>\n<\/tr>\n
\n

Keynote – Session 1<\/p>\n<\/td>\n

9:00<\/td>\nDario AZZIMONTI <\/a>(IDSIA)
A short overview of preference learning with Gaussian process based approaches<\/strong><\/em>
Abstract<\/a> – Slides<\/a><\/td>\n<\/tr>\n
\n

Student – Session 1<\/p>\n

Chair: Sidonie Lefebvre<\/strong><\/p>\n<\/td>\n

9:50<\/td>\nGuillaume\u00a0CHENNETIER
Adaptive importance sampling of stochastic processes with graph-based mean hitting times<\/strong><\/em>
Abstract<\/a> – Slides<\/a><\/td>\n<\/tr>\n
\u00a0<\/td>\n10:20<\/td>\nCoffee break<\/td>\n<\/tr>\n
\n

Student – Session 2<\/p>\n

Chair: Bertrand Iooss<\/strong><\/p>\n<\/td>\n

10:45<\/td>\nClement\u00a0DUHAMEL
Sampling criteria for excursion set estimation on multi-output models<\/strong><\/em>
Abstract<\/a> – Slides<\/a><\/td>\n<\/tr>\n
11:15<\/td>\nRomain AIT ABDELMALEK-LOMENECH\u00a0
A stepwise uncertainty reduction strategy for the estimation of small quantile sets<\/em><\/strong>
Abstract<\/a> – Slides<\/a><\/td>\n<\/tr>\n
11:45<\/td>\nPoster blitz<\/td>\n<\/tr>\n
\u00a0<\/td>\n12:15<\/td>\nLunch<\/td>\n<\/tr>\n
\n

Student – Session 3<\/p>\n

Chair: Julien Reygner<\/strong><\/p>\n<\/td>\n

14:00<\/td>\nTanguy\u00a0APPRIOU\u00a0
High-Dimensional Bayesian Optimization with a Combination of Kriging models<\/strong><\/em>
Abstract <\/a>– Slides<\/a><\/td>\n<\/tr>\n
14:30<\/td>\nFanny\u00a0LEHMANN\u00a0
Quantifying uncertainties in seismic waves propagation with a Fourier Neural Operator surrogate model<\/strong><\/em>
Abstract<\/a> – Slides<\/a><\/td>\n<\/tr>\n
15:00<\/td>\nPoster blitz<\/td>\n<\/tr>\n
\u00a0<\/td>\n15:30<\/td>\nCoffee break<\/td>\n<\/tr>\n
\n

Student – Session 4<\/p>\n

Chair: S\u00e9bastien Da Veiga<\/strong><\/p>\n<\/td>\n

16:00<\/td>\nJulien\u00a0DEMANGE-CHRYST\u00a0
Variational autoencoder with weighted samples for high-dimensional non-parametric adaptive importance sampling<\/em><\/strong>
Abstract<\/a> – Slides<\/a><\/td>\n<\/tr>\n
16:30<\/td>\nNo\u00e9\u00a0FELLMANN\u00a0
Sensitivity analysis of set-valued models<\/em><\/strong>
Abstract<\/a> – Slides<\/a><\/td>\n<\/tr>\n
17:00<\/td>\nPaul LARTAUD\u00a0
I-optimal sequential design for Bayesian inverse problems with Gaussian process surrogate models<\/em><\/strong>
Abstract<\/a> – Slides<\/a><\/td>\n<\/tr>\n
Posters – Session 1<\/td>\n17:30 – 19:00<\/td>\nPoster session<\/td>\n<\/tr>\n
\u00a0<\/td>\n19:00<\/td>\nCocktail<\/td>\n<\/tr>\n
\u00a0<\/td>\n19:45<\/td>\nDinner<\/td>\n<\/tr>\n
\u00a0<\/td>\n<\/tr>\n
Thursday, April 4<\/strong><\/td>\n<\/tr>\n
\n

Tutorial – Part. 1<\/p>\n

Chair: Guillaume Perrin<\/strong><\/p>\n<\/td>\n

8:30<\/td>\nAnna Maria Massone\u00a0
Image reconstruction methods and machine learning techniques: applications to astronomical imaging and space weather<\/em><\/strong> (Tutorial Part 1)
Abstract<\/a> – Intro – Slides<\/a><\/td>\n<\/tr>\n
\u00a0<\/td>\n9:45<\/td>\nCoffee break<\/td>\n<\/tr>\n
\n

Keynote – Session 2
Chair: Miguel Munoz Zuniga<\/strong><\/p>\n<\/td>\n

10:20<\/td>\nChiara Tommasi\u00a0
Optimal design of experiments for model discrimination<\/em><\/strong>
Abstract<\/a> – Slides<\/a><\/td>\n<\/tr>\n
11:10<\/td>\nAlessandro Rudi\u00a0
Kernel Methods in the quest for adaptivity in infinite-dimensional optimisation with dense conic constraints: with application in non-convex optimisation, optimal transport and beyond<\/em><\/strong>
Abstract<\/a> – Slides<\/a><\/td>\n<\/tr>\n
\u00a0<\/td>\n12:00<\/td>\nLunch<\/td>\n<\/tr>\n
Keynote – Session 3
Chair: Victor Picheny<\/strong><\/td>\n		14:00<\/td>\nHenry Moss<\/a>\u00a0
An Automatic Climate Scientist: Using Gaussian processes to uncover the secrets of the universe<\/em><\/strong>
Abstract<\/a> – Slides<\/a><\/td>\n<\/tr>\n
14:50<\/td>\nAretha Teckentrup\u00a0
Smoothed circulant embedding and applications in multilevel Monte Carlo methods<\/em><\/strong>
Abstract<\/a> – Slides<\/a><\/td>\n<\/tr>\n
\u00a0<\/td>\n15:40<\/td>\nCoffee break<\/td>\n<\/tr>\n
\n

Keynote – Session 4
Chair: Cl\u00e9mentine Prieur<\/strong><\/p>\n<\/td>\n

16:10<\/td>\nPatricia Reynaud-Bouret\u00a0
Theoretical and practical implications of the Kalikow decomposition in the study of neuronal networks: simulation, statistics and learning<\/em><\/strong>
Abstract<\/a> – Slides<\/a><\/td>\n<\/tr>\n
\u00a0Posters – Session 2<\/td>\n17:00 – 18:30<\/td>\nPoster session<\/td>\n<\/tr>\n
\u00a0<\/td>\n19:45<\/td>\nDinner<\/td>\n<\/tr>\n
\u00a0<\/td>\n<\/tr>\n
Friday, April 5<\/strong><\/td>\n<\/tr>\n
\n

Tutorial – Part. 2<\/p>\n

Chair: C\u00e9line Helbert
<\/strong><\/p>\n<\/td>\n

8:30<\/td>\nAnna Maria Massone\u00a0
Image reconstruction methods and machine learning techniques: applications to astronomical imaging and space weather<\/em><\/strong> (Tutorial Part 2)
Abstract<\/a> – Slides<\/td>\n<\/tr>\n
\u00a0<\/td>\n9:45<\/td>\nAwards & Coffee break<\/td>\n<\/tr>\n
\n

Keynote – Session 5
Chair: Julien Bect<\/strong><\/p>\n<\/td>\n

10:20<\/td>\nAnthony Nouy\u00a0
Optimal sampling for linear and nonlinear approximation<\/em><\/strong>
Abstract<\/a> – Slides<\/a><\/td>\n<\/tr>\n
11:10<\/td>\nMaria Strazzullo\u00a0
Reduced Order Methods for Parametric Optimal Control: an Overview and Diverse Applications<\/em><\/strong>
Abstract<\/a> – Slides<\/a><\/td>\n<\/tr>\n
\u00a0<\/td>\n12:00<\/td>\nMascot-Num Talk<\/td>\n<\/tr>\n
\u00a0<\/td>\n12:15<\/td>\nLunch*<\/td>\n<\/tr>\n
\u00a0<\/td>\n14:00<\/td>\nEnd of Mascot-Num<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

*lunch-box will be available for participants who have to leave early. Please let us know when registering on Tuesday.<\/em><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Tuesday, April 2 \u00a0 17:00 Room check-in \u00a0 18:00 Conference registration \u00a0 19:45 Dinner \u00a0 Wednesday, April 3 \u00a0 8:30 Opening Keynote – Session 1 9:00 Dario AZZIMONTI (IDSIA) A short overview of preference learning with Gaussian process based approachesAbstract – Slides Student – Session 1 Chair: Sidonie Lefebvre 9:50\u2026<\/p>\n

Continue reading<\/span><\/i><\/a> <\/p>\n","protected":false},"author":539,"featured_media":145,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"templates\/template-page-with-intro.php","meta":{"footnotes":""},"class_list":["post-72","page","type-page","status-publish","has-post-thumbnail","hentry"],"_links":{"self":[{"href":"https:\/\/project.inria.fr\/mascotnum2024\/wp-json\/wp\/v2\/pages\/72","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/project.inria.fr\/mascotnum2024\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/project.inria.fr\/mascotnum2024\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/mascotnum2024\/wp-json\/wp\/v2\/users\/539"}],"replies":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/mascotnum2024\/wp-json\/wp\/v2\/comments?post=72"}],"version-history":[{"count":63,"href":"https:\/\/project.inria.fr\/mascotnum2024\/wp-json\/wp\/v2\/pages\/72\/revisions"}],"predecessor-version":[{"id":861,"href":"https:\/\/project.inria.fr\/mascotnum2024\/wp-json\/wp\/v2\/pages\/72\/revisions\/861"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/mascotnum2024\/wp-json\/wp\/v2\/media\/145"}],"wp:attachment":[{"href":"https:\/\/project.inria.fr\/mascotnum2024\/wp-json\/wp\/v2\/media?parent=72"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}