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X-WR-CALDESC:Events for CREST
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DTSTART:20220327T010000
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DTSTART;TZID=Europe/Helsinki:20221010T121500
DTEND;TZID=Europe/Helsinki:20221010T133000
DTSTAMP:20260726T111128
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SUMMARY:Fabrice COLLARD (TSE ) "Monetary Policy and Endogenous Financial Crises"
DESCRIPTION:The Macroeconomics Seminar:\nTime: 12:15 pm – 13:30 pm\nDate: 10th of October 2022 \nRoom 3001 \nFabrice COLLARD (TSE ) “Monetary Policy and Endogenous Financial Crises” \nAbstract : We study whether a central bank should deviate from its objective of price stability to promote financial stability. We tackle this question within a textbook New Keynesian model augmented with capital accumulation and microfounded endogenous financial crises. We compare several interest rate rules\, under which the central bank responds more or less forcefully to inflation and aggregate output. Our main findings are threefold. First\, monetary policy affects the probability of a crisis both in the short run (through aggregate demand) and in the medium run (through savings and capital accumulation). Second\, a central bank can both reduce the probability of a crisis and increase welfare by departing from strict inflation targeting and responding systematically to fluctuations in output. Third\, financial crises may occur after a long period of unexpectedly loose monetary policy as the central bank abruptly reverses course. \nJoint work : F. Boissay\, J. Galí et C. Manéa \nOrganizers:\nOlivier LOISEL (CREST) \nSponsors:\nCREST \n
URL:https://crest.science/event/fabrice-collard-tse-t-b-a/
CATEGORIES:Macroeconomics
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DTSTART;TZID=Europe/Helsinki:20221010T140000
DTEND;TZID=Europe/Helsinki:20221010T150000
DTSTAMP:20260726T111128
CREATED:20221005T074437Z
LAST-MODIFIED:20221005T074529Z
UID:14112-1665410400-1665414000@crest.science
SUMMARY:Yu Lu (CREST-ENSAE) "Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance"
DESCRIPTION:Statistical Seminar: Every Monday.\nTime: 2:00 pm\nDate: 10 of October 2022\nPlace: Room 3001 \nYu Lu (CREST-ENSAE) “Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance” \nAbstract:We study stochastic convex optimization under infinite noise variance. Specifically\, when the stochastic gradient is unbiased and has uniformly bounded (1 + κ)-th moment\, for some κ ∈ (0\, 1]\, we quantify the convergence rate of the Stochastic Mirror Descent algorithm with a particular class of uniformly convex mirror maps\, in terms of the number of iterations\, dimensionality and related geometric parameters of the optimization problem. Interestingly this algorithm does not require any explicit gradient clipping or normalization\, which have been extensively used in several recent empirical and theoretical works. We complement our convergence results with information-theoretic lower bounds showing that no other algorithm using only stochastic first-order oracles can achieve improved rates. Our results have several interesting consequences for devising online/streaming stochastic approximation algorithms for problems arising in robust statistics and machine learning. \n  \nOrganizers:\nCristina BUTUCEA (CREST)\, Alexandre TSYBAKOV (CREST)\, Karim LOUNICI (CMAP) \, Jaouad MOURTADA (CREST)\nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/shayn-mukherjee-university-of-leipzig-modeling-shapes-and-fields-2/
CATEGORIES:Statistics
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