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X-WR-CALDESC:Events for CREST
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DTSTART:20220327T010000
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DTSTART;TZID=Europe/Helsinki:20220328T121500
DTEND;TZID=Europe/Helsinki:20220328T133000
DTSTAMP:20260715T051723
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LAST-MODIFIED:20220321T091558Z
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SUMMARY:Thomas LAMBERT (Rotterdam School of Management\, Erasmus University) "(In)dependent Central Banks"
DESCRIPTION:The Macroeconomics Seminar:\nTime: 12:15 pm – 13:30 pm\nDate: 28th of March 2022 \nRoom \nThomas LAMBERT (Rotterdam School of Management\, Erasmus University) “(In)dependent Central Banks” \nAbstract : Over the past three decades\, many countries reformed the institutional framework governing their central banks to increase their operational independence from the executive and elected politicians. Collecting systematic biographical information\, international press coverage\, and independent expert opinions\, we show that over the same period appointments of central bank governors have become more politically motivated\, especially after significant institutional reforms aiming to insulate central banks and their governors from political interference. We show that such politically motivated appointments are positively correlated with lack of de facto independence\, as measured by expert opinions and central bank governor turnover. \nOrganizers:\nAlessandro RIBONI (Polytechnique) \nSponsors:\nCREST \n
URL:https://crest.science/event/thomas-lambert-rotterdam-school-of-management-erasmus-university-t-b-a/
CATEGORIES:Macroeconomics
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DTSTART;TZID=Europe/Helsinki:20220328T140000
DTEND;TZID=Europe/Helsinki:20220328T151500
DTSTAMP:20260715T051723
CREATED:20220324T124610Z
LAST-MODIFIED:20220324T124610Z
UID:13538-1648476000-1648480500@crest.science
SUMMARY:Julien CHHOR et Flore SENTENAC (ENSAE-CREST) - "Robust estimation of discrete distributions under local differential privacy "
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:15 pm\nDate: 28th of March 2022\nPlace: Amphi 200 \nJulien CHHOR et Flore SENTENAC (ENSAE-CREST) – “Robust estimation of discrete distributions under local differential privacy” \nAbstract: Although robust learning and local differential privacy are both widely studied fields of research\, combining the two settings is an almost unexplored topic. We consider the problem of estimating a discrete distribution in total variation from n contaminated data batches under a local differential privacy constraint. A fraction 1−ϵ of the batches contain k i.i.d. samples drawn from a discrete distribution p over d elements. To protect the users’ privacy\, each of the samples is privatized using an α-locally differentially private mechanism. \n The remaining ϵn batches are an adversarial contamination. The minimax rate of estimation under contamination alone\, with no privacy\, is known\, up to a √ log(1/ϵ) factor. Under the privacy constraint alone\, the minimax rate of estimation is also known. We characterize the minimax estimation rate under the two constraints up to a √ log(1/ϵ) factor\, which is larger than the sum of the two separate rates. We provide a polynomial-time algorithm achieving this bound\, as well as a matching information theoretic lower bound. \n  \nOrganizers:\nCristina BUTUCEA (CREST)\, Alexandre TSYBAKOV (CREST)\, Karim LOUNICI (CMAP) \, Jaouad MOURTADA (CREST)\nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/julien-chhor-et-flore-sentenac-ensae-crest-robust-estimation-of-discrete-distributions-under-local-differential-privacy/
CATEGORIES:Statistics
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