BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CREST - ECPv5.1.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:CREST
X-ORIGINAL-URL:https://crest.science
X-WR-CALDESC:Events for CREST
BEGIN:VTIMEZONE
TZID:Europe/Helsinki
BEGIN:DAYLIGHT
TZOFFSETFROM:+0200
TZOFFSETTO:+0300
TZNAME:EEST
DTSTART:20220327T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0300
TZOFFSETTO:+0200
TZNAME:EET
DTSTART:20221030T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20220530T140000
DTEND;TZID=Europe/Helsinki:20220530T151500
DTSTAMP:20240223T154018
CREATED:20220311T132153Z
LAST-MODIFIED:20220517T060450Z
UID:13472-1653919200-1653923700@crest.science
SUMMARY:Subhro GHOSH (National University of Singapore ) - "The unreasonable effectiveness of determinantal processes"
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:15 pm\nDate: 30th of May 2022\nPlace: Amphi 200 \nSubhro GHOSH (National University of Singapore ) – “The unreasonable effectiveness of determinantal processes” \nAbstract: In 1960\, Wigner published an article famously titled “The Unreasonable Effectiveness of Mathematics in the Natural Sciences”. In this talk we will\, in a small way\, follow the spirit of Wigner’s coinage\, and explore the unreasonable effectiveness of determinantal processes (a.k.a. DPPs) far beyond their context of origin. DPPs originated in quantum and statistical physics\, but have emerged in recent years to be a powerful toolbox for many fundamental learning problems. In this talk\, we aim to explore the breadth and depth of these applications. On one hand\, we will explore a class of Gaussian DPPs and the novel stochastic geometry of their parameter modulation\, and their applications to the study of directionality in data and dimension reduction. At the other end\, we will consider the fundamental paradigm of stochastic gradient descent\, where we leverage connections with orthogonal polynomials to design a minibatch sampling technique based on data-sensitive DPPs ; with provable guarantees for a faster convergence exponent compared to traditional sampling. Based on the following works. \n[1] Gaussian determinantal processes: A new model for directionality in data\, with P. Rigollet\, Proceedings of the National Academy of Sciences\, vol. 117\, no. 24 (2020)\, pp. 13207–13213 (PNAS Direct Submission)\n[2] Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD\, with R. Bardenet and M. Lin\nAdvances in Neural Information Processing Systems 34 (Spotlight at NeurIPS 2021) \n \nOrganizers:\nCristina BUTUCEA (CREST)\, Alexandre TSYBAKOV (CREST)\, Karim LOUNICI (CMAP) \, Jaouad MOURTADA (CREST)\nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/subhro-ghosh-tba/
CATEGORIES:Statistics
ATTACH;FMTTYPE=:
END:VEVENT
END:VCALENDAR