BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CREST - ECPv5.1.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:CREST
X-ORIGINAL-URL:https://crest.science
X-WR-CALDESC:Events for CREST
BEGIN:VTIMEZONE
TZID:Europe/Helsinki
BEGIN:DAYLIGHT
TZOFFSETFROM:+0200
TZOFFSETTO:+0300
TZNAME:EEST
DTSTART:20250330T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0300
TZOFFSETTO:+0200
TZNAME:EET
DTSTART:20251026T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20250513T100000
DTEND;TZID=Europe/Helsinki:20250520T230000
DTSTAMP:20260710T113854
CREATED:20250506T063233Z
LAST-MODIFIED:20250506T063233Z
UID:18060-1747130400-1747782000@crest.science
SUMMARY:François HU (Milliman)  "Obtaining Fair Insurance Premiums with Multiple Sensitive Attributes"
DESCRIPTION:Finance-Insurance\nTime: 10.00 am\nDate:13th of May  2025\nRoom 3001 \nFrançois HU (Milliman) “Obtaining Fair Insurance Premiums with Multiple Sensitive Attributes” \nAbstract : In the context of Algorithmic Fairness\, the goal is to ensure that sensitive attributes have no influence on decision-making outcomes. This objective has driven the development of various fairness definitions and tools\, which have been successfully applied across multiple domains\, including insurance. However\, the challenge becomes more complex when dealing with multiple sensitive attributes. In the absence of intentional discrimination\, predictive models used in insurance – for tasks such as pricing\, fraud detection\, or claims estimation – can inadvertently result in biased decisions\, such as ageism\, racism\, or sexism. As emphasized by Kearns et al. (2019)\, machine learning models do not inherently provide fairness unless explicitly designed to do so. To address this challenge\, we propose a novel sequential framework that progressively achieves fairness across multiple sensitive attributes in insurance applications. By leveraging multi-marginal Wasserstein barycenters\, we extend the concept of Strong Demographic Parity to ensure independence between multiple sensitive features and decision outcomes. Our approach provides a closed-form solution for an optimal\, sequentially fair predictor\, while maintaining interpretability in terms of how the sensitive features interact. Additionally\, our framework accommodates the trade-offs between fairness and risk\, enabling targeted prioritization of fairness improvements for specific attributes based on context-specific requirements. We demonstrate the practical applicability of our framework through a data-driven estimation procedure applied to both synthetic and real-world datasets\, including an insurance dataset. Our numerical experiments show that this post-processing method not only enhances fairness in decision-making but also ensures transparency and maintains statistical guarantees\, offering a robust solution for achieving fairer insurance premiums.\n \nOrganizers:  Jean-David FERMANIAN \n  \n
URL:https://crest.science/event/francois-hu-milliman-obtaining-fair-insurance-premiums-with-multiple-sensitive-attributes/
CATEGORIES:Finance-Insurance,Seminars
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20250513T160000
DTEND;TZID=Europe/Helsinki:20250520T170000
DTSTAMP:20260710T113854
CREATED:20250506T064610Z
LAST-MODIFIED:20250506T064610Z
UID:18061-1747152000-1747760400@crest.science
SUMMARY:Antonio OCELLO (Ecole Polytechnique)  "Convergence Analysis of Diffusion Models: Towards Reliable Sampling"
DESCRIPTION:Finance-Insurance\nTime: 4.00 p.m.\nDate:13th of May  2025\nRoom 3001 \nAntonio OCELLO (Ecole Polytechnique) “Convergence Analysis of Diffusion Models: Towards Reliable Sampling” \nAbstract : Generative models are increasingly explored in insurance for tasks such as risk simulation\, scenario generation\, and synthetic data augmentation. Their usefulness hinges on the ability to reproduce stylized features of actuarial and claims data—such as heavy tails\, skewed marginals\, and rare event structures—essential for solvency analysis and pricing under uncertainty. Among the available methods\, Score-Based Generative Models (SGMs)\, also known as diffusion models\, offer a flexible framework to sample from complex\, high-dimensional distributions. However\, a key challenge lies in rigorously understanding their convergence.\nIn this talk\, I will present recent advances in the theoretical analysis of SGMs\, focusing on convergence guarantees relevant for actuarial applications. First\, I will show how the choice of the noise schedule impacts generative performance\, and provide explicit bounds on KL divergence and Wasserstein-2 distance. Second\, I will introduce a new convergence analysis in Wasserstein-2 distance\, based on the Ornstein–Uhlenbeck process\, that remains valid beyond log-concave settings—such as for mixtures of Gaussians. Finally\, I will discuss some open problems in the simulation of data for insurance purposes.\nThis talk is based on joint work with Stanislas Strasman\, Claire Boyer\, Sylvain Le Corff\, and Vincent Lemaire (TMLR 2024 – https://openreview.net/forum?id=BlYIPa0Fx1)\, as well as a recent collaboration with Marta Gentiloni-Silveri (ICML 2025 – https://arxiv.org/pdf/2501.02298).\n\n \nOrganizers:  Jean-David FERMANIAN \n  \n
URL:https://crest.science/event/antonio-ocello-ecole-polytechnique-convergence-analysis-of-diffusion-models-towards-reliable-sampling/
CATEGORIES:Finance-Insurance,Seminars
ATTACH;FMTTYPE=:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20250520T121500
DTEND;TZID=Europe/Helsinki:20250520T133000
DTSTAMP:20260710T113854
CREATED:20250401T074651Z
LAST-MODIFIED:20250513T083934Z
UID:18015-1747743300-1747747800@crest.science
SUMMARY:Eva RAIBER (AMSE) - "For Better or for Babies: Fertility Constraints and Marriage in China"
DESCRIPTION:Applied Micro Seminar : Every Tuesday \nTime: 12:15 pm – 13:30 pm\nDate: 20th of May\nRoom : 3001 \n  \nEva RAIBER (AMSE) – “For Better or for Babies: Fertility Constraints and Marriage in China” with Lucie Giorgi \n  \nAbstract : “Can fertility policies have unintended effects on who gets married ? We investigate the effect of the 2015 relaxation of China’s one-child policy on marriage outcomes. Before universal permission for two children\, certain groups were already allowed to have two children. At the same time\, China’s sex ratio is highly skewed towards more marriageable men than women. Being allowed to have a second child could be a valuable characteristic in the marriage market\, increasing men’s chances of marriage. Previously advantaged men might then lose out from the relaxation of the one-child policy as they lose their marriage market advantage. Using detailed policy data on exemptions from the one-child limit and individual data from 2010–2018\, we find that after the relaxation men who were previously allowed to have a second child are less likely to get married. There is no effect on women. The effect is concentrated within counties with high fertility rates and provinces with a high sex imbalance. The results suggest that differential fertility constraints distorted who got married by giving those allowed to have a second child an advantage. We also find that provinces where more people were exempted see an increase in positive assortative marriages after the relaxation\, suggesting distortions also on who married whom”. \n  \n  \nOrganizers:\nBenoît SCHMUTZ (Pôle économie du CREST)\nClément MALGOUYRES (Pôle économie du CREST) \nSponsors:\nCREST \n
URL:https://crest.science/event/https-www-amse-aixmarseille-fr-fr-membres-raiber/
CATEGORIES:Applied Seminar,Seminars
ATTACH;FMTTYPE=:
END:VEVENT
END:VCALENDAR