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DTSTART:20260329T010000
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DTSTART;TZID=Europe/Helsinki:20260209T140000
DTEND;TZID=Europe/Helsinki:20260209T153000
DTSTAMP:20260711T112931
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LAST-MODIFIED:20260203T152324Z
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SUMMARY:Alexandra CARPENTIER (Universität Potsdam) - Statistical and computational challenges in unsupervised learning: focus on ranking
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:00 pm\nDate: 9th Febuary\nPlace: 3001 \n  \nAlexandra CARPENTIER (Universität Potsdam) – Statistical and computational challenges in unsupervised learning: focus on ranking \n  \n  \n Abstract:  \nRanking problems are prevalent in modern statistical\, machine learning\, and computer science literature. This includes a variety of practical situations ranging from ranking experts/workers in crowd-sourced data\, ranking players in a tournament or equivalently sorting objects based on pairwise comparisons. A main challenge in this field is to construct an estimator of the rank of the experts\, based on incomplete and noisy data. \nIn this talk\, we focus on understanding the problem of ranking both from an informational – namely\, characterizing the fundamental statistical thresholds for optimal estimation – and a computational – namely\, also characterising the fundamental limits of computationally efficient estimation – perspective. A core question for these problems is on whether statistical optimality is compatible with computational efficiency. \nTo do that\, we first consider the simpler sub-problem of sub-matrix detection and estimation\, which is useful to apprehend the more complex problem of ranking – and we will particularly focus on computational lower bounds. Based on results for this problem\, we explain how they can be used to solve the more challenging problem of ranking. \n  \n  \n  \nOrganizers: \nAnna KORBA (CREST)\, Vincent DIVOL (CREST) \, Jaouad MOURTADA (CREST) \n  \n  \nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/alexandra-carpentier-universitat-potsdam-tba/
CATEGORIES:Seminars,Statistics
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DTSTART;TZID=Europe/Helsinki:20260209T160000
DTEND;TZID=Europe/Helsinki:20260209T171500
DTSTAMP:20260711T112931
CREATED:20260121T135123Z
LAST-MODIFIED:20260202T080520Z
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SUMMARY:Julien MONARDO (University of Bristol) - "Estimating Nesting Structures"
DESCRIPTION:PSE Seminar : \nTime: 16:00 pm – 17:15 pm\nDate: 9th of february\nRoom : 3001 \n  \nJulien MONARDO (University of Bristol) –  “Estimating Nesting Structures” with Ali Hortaçsu\, Jonas Lieber\, and Áureo de Paula \n  \nAbstract : \nThe nested logit model is commonly used to estimate demand in differentiated products markets. However\, it and its generalizations require an assumed nesting structure. In this paper\, we propose to estimate the nesting structure from the data. For this\, we build on a recent generalization of the nested logit model that allows any possible nesting structure and is consistent with utility-maximization by heterogeneous consumers. In this setting\, estimating the nesting structure amounts to estimating a linear model with many endogeneous variables\, which is challenging. We show theoretically and in simulations that non-negativity constraints coming from economic theory are sufficient to recover the nesting structure from data. In doing so\, we explore the regularization properties of the non-negative least squares estimator as demonstrated in the statistical literature and expanded here to an instrumental variable context. This estimator may be of independent interest. \n  \nOrganizer :\nLaurent DAVEZIES (Pôle économie du CREST) \nSponsors:\nCREST \n
URL:https://crest.science/event/https-sites-google-com-site-julienmonardoeconomics/
CATEGORIES:Paris Econometrics Seminar,Seminars
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