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:20210328T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0300
TZOFFSETTO:+0200
TZNAME:EET
DTSTART:20211031T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20211011T150000
DTEND;TZID=Europe/Helsinki:20211011T161500
DTSTAMP:20210928T045827
CREATED:20210906T081620Z
LAST-MODIFIED:20210917T123007Z
UID:12954-1633964400-1633968900@crest.science
SUMMARY:Jonathan Scarlett (National University of Singapore) - "Recent Developments in High-Dimensional Estimation with Generative Priors"
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 3:00 pm – 4:15 pm exceptionally\nDate: 11th of October 2021\nPlace: visio \nJonathan Scarlett (National University of Singapore) – “Recent Developments in High-Dimensional Estimation with Generative Priors” \nAbstract: The problem of estimating an unknown vector (or image) from linear or non-linear measurements has a long history in statistics\, machine learning\, and signal processing. Classical studies focus on the “n >> p” regime (#measurements >> #parameters)\, and more recent studies handle the “n << p” regime by exploiting low-dimensional structure such as sparsity or low-rankness. Such variants are commonly known as compressive sensing. In this talk\, I will verview recent methods that move beyond these explicit notions of structure\, and instead assume that the underlying vector is well-modeled by a data-riven generative model (e.g.\, produced by deep learning methods such as GANs). I will focus primarily on theoretical developments\, including upper and lower bounds on the sample complexity in terms of various properties of the generative model\, such as its number of latent (input) parameters\, its Lipschitz constant\, and its width and depth in the special case of neural network models. I will also discuss some developments regarding non-linear models\, geometric roperties of the relevant optimization landscapes\, and methods for fully general probabilistic priors. \nOrganizers:\nCristina BUTUCEA (CREST)\, Alexandre TSYBAKOV (CREST)\, Karim LOUNICI (CMAP) \, Jaouad MOURTADA (CREST)\nSponsors:\nCREST-CMAP \n\n
URL:https://crest.science/event/jonathan-scarlett-national-university-of-singapore-recent-developments-in-high-dimensional-estimation-with-generative-priors/
CATEGORIES:Statistics
ATTACH;FMTTYPE=:
END:VEVENT
END:VCALENDAR