BEGIN:VCALENDAR PRODID:-//eluceo/ical//2.0/EN VERSION:2.0 CALSCALE:GREGORIAN BEGIN:VEVENT UID:887904d6cfa3160d7aa613b6264f8b82 DTSTAMP:20261107T062915Z SUMMARY:LIDAM Statistics Seminar by Ivan Kojadinovic DESCRIPTION:27/11/2026 - 11:00 - ISBA C115 \; \;Ivan Kojadinovic &n bsp\;(Université de Pau et des Pays de l'Adour) \;Will give a present ation on : \;An algorithmic procedure for solving the generalized mini mum information checkerboard copula problemAbstract: \;The minimum inf ormation copula principle (see Meeuwissen and Bedford\, 1997) is a maximum entropy-like approach for finding the least informative copula that satis fies a certain number of expectation constraints specified either from exp ert knowledge or the available limited data. In this presentation\, we fir st propose a generalization of this principle allowing the inclusion of ad ditional constraints fixing certain higher-order margins of the copula. We next prove that the associated optimization problem has a unique solution under a natural condition. As the latter problem is intractable in genera l\, following the existing literature\, we consider its version with all t he probability measures involved in its formulation replaced by checkerboa rd approximations. This amounts to attempting to solve aso-called discrete I-projection linear problem. We then use the seminal results of Csiszar ( 1975) to derive an IPFP-like procedure for solving the latter and provide theoretical guarantees for its convergence. We next report numerical exper iments in dimensions up to four with substantially finer discretizations t han those encountered in the literature. We finally discuss the computatio nal cost of the procedure and hint at memory saving implementations that s hould allow to tackle higher dimensional problems under certain conditions . \; URL:https://uclouvain.be/fr/calendar/isba DTSTART;TZID=Europe/Brussels:20261127T110000 DTEND;TZID=Europe/Brussels:20261127T120000 LOCATION:Voie du Roman Pays\, 20 1348 Louvain-la-Neuve END:VEVENT BEGIN:VEVENT UID:e6705840039818fea8c4d4363b7c6348 DTSTAMP:20261107T062915Z SUMMARY:LIDAM Statistics Seminar by Kellie Archer DESCRIPTION:13/11/2026 - 14:30 - ISBA C.115 \;Kellie Archer \;(The Ohio State University) \;Will give a presentation on : \;High-Dime nsional Mixture Cure Models for Time-to-Event Data \;Abstract: \;M edical breakthroughs in recent decades have led to cures for many diseases including cancer.  \;For example\, various groups have shown that adv ances in therapy for leukemia and myelodysplastic syndrome have increased overall survival rates and some groups have identified factors related to long-term survival\, defined as survival exceeding three years\, which inc luded that patients treated on newer treatment regimens were more likely t o be long-term survivors. In fact\, some argue that these improved outcome s indicate that some AML patients can be considered “potentially cured. ” The mixture cure model (MCM) is a time-to-event model that is used whe n a cured fraction exists. MCMs assume the population consists of two subg roups\, those cured will not experience the event of interest and those su sceptible to the event of interest. Therefore\, “cured” can be conside red synonymous with attaining long-term relapse-free survival. Thus\, ther e are two regression components in MCMs which permit identification of fea tures associated with cure and/or latency of susceptible patients. Many re searchers have sought to identify a prognostic model for a time-to-event o utcome. When the covariate space is high-dimensional\, as in the case with gene expression data\, typically penalized Cox proportional hazards (PH) models are fit. However\, when a dataset includes a cured fraction\, MCMs are more appropriate than Cox PH models. In these scenarios\, Cox models o ften yield inaccurate hazard and survival estimates due to violations of t he proportional hazards assumption. Despite their utility\, robust methods for fitting MCMs to high-dimensional data remain scarce. In this talk\, I will present our recent work extending MCMs to effectively handle high-di mensional covariate spaces\, offering a more accurate prognostic tool for modern high-dimensional datasets. \; URL:https://uclouvain.be/fr/calendar/isba DTSTART;TZID=Europe/Brussels:20261113T143000 DTEND;TZID=Europe/Brussels:20261113T153000 LOCATION:Voie du Roman Pays\, 20 1348 Louvain-la-Neuve END:VEVENT BEGIN:VEVENT UID:b8695d3e86de584aeaabcd7da8be2fac DTSTAMP:20261107T062915Z SUMMARY:Applied Statistics Workshop by Aurelie Bertrand and Laura Symul DESCRIPTION:More details coming soon URL:https://uclouvain.be/fr/calendar/isba DTSTART;TZID=Europe/Brussels:20261127T143000 DTEND;TZID=Europe/Brussels:20261127T153000 LOCATION:ISBA - C115 (1st Floor) 1348 Louvain-la-Neuve END:VEVENT BEGIN:VEVENT UID:0ae4f7d3262925a29776f55fe360708e DTSTAMP:20261107T062915Z SUMMARY:LIDAM Statistics Seminar by Alessandra Luati DESCRIPTION:11/12/2026 - 14:30 - ISBA C115 \; \;Alessandra Luati &n bsp\;(Imperial College London) \;More details coming soon \; URL:https://uclouvain.be/fr/calendar/isba DTSTART;TZID=Europe/Brussels:20261211T143000 DTEND;TZID=Europe/Brussels:20261211T153000 LOCATION:Voie du Roman Pays\, 20 1348 Louvain-la-Neuve END:VEVENT BEGIN:VEVENT UID:6d168850ad120bfa12d70f79f5a7a221 DTSTAMP:20261107T062915Z SUMMARY:Applied Statistics Workshop by Marina Vives DESCRIPTION:More details coming soon \; URL:https://uclouvain.be/fr/calendar/isba DTSTART;TZID=Europe/Brussels:20270212T143000 DTEND;TZID=Europe/Brussels:20270212T153000 LOCATION:ISBA - C115 (1st Floor) 1348 Louvain-la-Neuve END:VEVENT BEGIN:VEVENT UID:2c361feb7eb3e169acc6b3b360a03500 DTSTAMP:20261107T062915Z SUMMARY:LIDAM Statistics Seminar by Debarghya Ghoshdastidar DESCRIPTION:26/02/2027 - 14:30 - ISBA C115 \; \;Debarghya Ghoshdast idar(Technical University of Munich) \; \;More details coming soon  \; URL:https://uclouvain.be/fr/calendar/isba DTSTART;TZID=Europe/Brussels:20270226T143000 DTEND;TZID=Europe/Brussels:20270226T153000 LOCATION:ISBA - C115 (1st Floor) 1348 Louvain-la-Neuve END:VEVENT BEGIN:VEVENT UID:539cf04167cda82592913e0c60004789 DTSTAMP:20261107T062915Z SUMMARY:LIDAM Statistics Seminar by Emilie Devijver DESCRIPTION:05/03/2027 - 14:30 - ISBA C115 \; \;Emilie Devijver(Uni versité Grenoble Alpes) \;will give a presentation on \;Causal in ference\, causal abstraction and causal machine learningAbstract:Causal in ference has been introduced to move beyond simple correlations\, offering capabilities for explainability\, generalization and action. \;In this talk\, I will present two main axis developped within our team: \;1/ causal abstraction\, as a practical means to encode expert knowledge in th e form of concepts (rather than variables) and to apply this knowledge eff ectively. To do so\, I will provide a formalism that enables causal infere nce while accounting for cycles\;2/ out-of-distribution generalization\, a nd how to leverage the causal structure to make predictions in a new envir onment through the causal invariance concept.This work is in collaboration with Marianne Clausel\, Eric Gaussier\, Théotime Le Goff\, Adèle Ribeir o and Clément Yvernes. \; URL:https://uclouvain.be/fr/calendar/isba DTSTART;TZID=Europe/Brussels:20270305T143000 DTEND;TZID=Europe/Brussels:20270305T153000 LOCATION: Voie du Roman Pays\, 20 1348 Louvain-la-Neuve END:VEVENT END:VCALENDAR