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DTSTART:20220327T010000
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DTSTART;TZID=Europe/Helsinki:20221121T121500
DTEND;TZID=Europe/Helsinki:20221121T133000
DTSTAMP:20260723T121322
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LAST-MODIFIED:20221107T061710Z
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SUMMARY:Axelle FERRIERE (PSE ) "On the Optimal Design of Transfers and Income-Tax Progressivity"
DESCRIPTION:The Macroeconomics Seminar:\nTime: 12:15 pm – 13:30 pm\nDate: 21th of Novembre 2022 \nRoom 3001 \nAxelle FERRIERE (PSE ) “On the Optimal Design of Transfers and Income-Tax Progressivity” \nAbstract : We study the optimal design of means-tested transfers and progressive income taxes. In a simple analytical model\, we show that adding a transfer to a loglinear tax induces welfare gains almost as large as in the second-best allocation. Transfers allow for more progressive average than marginal tax-and-transfer rates\, achieving redistribution while preserving efficiency. In a rich dynamic model\, we quantify the optimal fiscal plan. We use new flexible functions featuring targeted transfers and progressive income taxes\, proving a good empirical fit across the income distribution. Transfers should be larger than currently in the U.S. and financed with moderate income-tax progressivity. \nJoint work : P. Grübener\, G. Navarro and O. Vardishvili \nOrganizers:\nOlivier LOISEL (CREST) \nSponsors:\nCREST \n
URL:https://crest.science/event/axelle-ferriere-pse-t-b-a/
CATEGORIES:Macroeconomics
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DTSTART;TZID=Europe/Helsinki:20221121T140000
DTEND;TZID=Europe/Helsinki:20221121T151500
DTSTAMP:20260723T121322
CREATED:20221116T074829Z
LAST-MODIFIED:20221116T074829Z
UID:14276-1669039200-1669043700@crest.science
SUMMARY:Fanny YANG (ETH Zurich)- " How the strength of the inductive bias affects the generalization performance of interpolators "
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:15 pm\nDate: 21th of November 2022\nPlace: salle 3001 \nFanny YANG (ETH Zurich)- ” How the strength of the inductive bias affects the generalization performance of interpolators ” \nAbstract:Interpolating models have recently gained popularity in the statistical learning community due to common practices in modern machine learning: complex models achieve good generalization performance despite interpolating high-dimensional training data. In this talk\, we prove generalization bounds for high-dimensional linear models that interpolate noisy data generated by a sparse ground truth. In particular\, we first show that minimum-l1-norm interpolators achieve high-dimensional asymptotic consistency at a logarithmic rate. Further\, as opposed to the regularized or noiseless case\, for min-lp-norm interpolators with 1<p<2 we surprisingly obtain polynomial rates. Our results suggest a new trade-off for interpolating models: a stronger inductive bias encourages a simpler structure better aligned with the ground truth at the cost of an increased variance. We finally discuss our latest results\, where we show that this phenomenon also holds for nonlinear models. \n  \nOrganizers:\nCristina BUTUCEA (CREST)\, Alexandre TSYBAKOV (CREST)\, Karim LOUNICI (CMAP) \, Jaouad MOURTADA (CREST)\nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/fanny-yang-eth-zurich-how-the-strength-of-the-inductive-bias-affects-the-generalization-performance-of-interpolators/
CATEGORIES:Statistics
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