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X-WR-CALDESC:Events for CREST
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DTSTART:20250330T010000
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DTSTART;TZID=Europe/Helsinki:20250915T140000
DTEND;TZID=Europe/Helsinki:20250915T140000
DTSTAMP:20260715T032209
CREATED:20250903T091313Z
LAST-MODIFIED:20250903T091822Z
UID:18322-1757944800-1757944800@crest.science
SUMMARY:Vianney PERCHET (CREST) - Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:00 pm\nDate: 15th September\nPlace: 3001 \n  \nVianney PERCHET (CREST) – Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback \n  \n Abstract:  \nIn this talk\, I will introduce the problem of learning in zero-sum game\, and especially for the problem of “last-iterate” convergence\, unlike the traditional literature that looks at the average convergence (we argue it makes more sense). The interesting property is that the optimal rate is T^{-1/4} which is quite unusual (and unexpected) in this literature. \n  \n  \nOrganizers: \nAnna KORBA (CREST)\, Karim LOUNICI (CMAP) \, Jaouad MOURTADA (CREST) \nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/vianney-perchet-crest-last-iterate-convergence-for-uncoupled-learning-in-zero-sum-games-with-bandit-feedback/
CATEGORIES:Seminars,Statistics
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DTSTART;TZID=Europe/Helsinki:20250915T160000
DTEND;TZID=Europe/Helsinki:20250915T173000
DTSTAMP:20260715T032209
CREATED:20250908T080057Z
LAST-MODIFIED:20250908T080057Z
UID:18331-1757952000-1757957400@crest.science
SUMMARY:Lucas RESENDE (CREST) - "Unbiased Estimation of Multi-Way Gravity Models\, with Philippe Choné (CREST) and Guillaume Lecué (CREST and ESSEC)
DESCRIPTION:PSE Seminar : \nTime: 16:00 pm – 17:30 pm\nDate: 15th of September\nRoom : 3001 \n  \nLucas RESENDE (CREST) – “Unbiased Estimation of Multi-Way Gravity Models\, with Philippe Choné (CREST) and Guillaume Lecué (CREST and ESSEC) \n  \nAbstract : \n“Maximum likelihood estimators\, such as the Poisson Pseudo-Maximum Likelihood (PPML)\, suffer from the incidental parameter problem: a bias in the estimation of structural parameters that arises from the joint estimation of structural and nuisance parameters. To address this issue in multi-way gravity models\, we propose a novel\, asymptotically unbiased estimator. Our method reframes the estimation as a series of classification tasks and is agnostic to both the number and structure of fixed effects. In sparse data environments — common in the network formation literature — it is also computationally faster than PPML. We provide empirical evidence that our estimator yields more accurate point estimates and confidence intervals than PPML and its bias-correction strategies. These improvements hold even under model misspecification and are more pronounced in sparse settings. While PPML remains competitive in dense\, low-dimensional data\, our approach offers a robust alternative for multi-way models that scales efficiently with sparsity”. \n  \nOrganizer :\nLaurent DAVEZIES (Pôle économie du CREST) \nSponsors:\nCREST \n
URL:https://crest.science/event/https-crest-science-user-lucas-resende/
CATEGORIES:Paris Econometrics Seminar,Seminars
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