BEGIN:VCALENDAR
VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VTIMEZONE
TZID:Europe/Vienna
BEGIN:DAYLIGHT
DTSTART:20210328T030000
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
TZNAME:CEST
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20201025T020000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:CET
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260406T074240Z
UID:5fb2637f9d798315748536@ist.ac.at
DTSTART:20201123T140000
DTEND:20201123T150000
DESCRIPTION:Speaker: Marco Mondelli\nhosted by Kseniia Khudiakova\, Raimund
 o Saona\nAbstract: In a generalized linear model (GLM)\, the goal is to es
 timate a d-dimensional signal x from an n-dimensional observation of the f
 orm f(Ax\, w)\, where A is a design matrix and w is a noise vector. Well-k
 nown examples of GLMs include phase retrieval\, 1-bit compressed sensing\,
  and logistic regression. We focus on the high-dimensional setting in whic
 h both the number of measurements n and the signal dimension d diverge\, w
 ith their ratio tending to a fixed constant.Linear and spectral methods ar
 e two popular solutions to obtain an initial estimate\, which can also be 
 used as a warm start for other algorithms. In particular\, the linear esti
 mator is a data-dependent linear combination of the columns of the design 
 matrix\, and its analysis is quite simple\; the spectral estimator is the 
 principal eigenvector of a data-dependent matrix\, whose spectrum exhibits
  a phase transition. In this talk\, I will first analyze the spectral meth
 od and prove that it leads to the information-theoretically optimal thresh
 old for weak recovery in phase retrieval. Second\, I will show how to opti
 mally combine the linear and spectral estimators. Finally\, I will conside
 r estimators based on approximate message passing (AMP) and prove how to i
 nitialize them with the spectral method.Based on joint work with Andrea Mo
 ntanari\, Christos Thrampoulidis and Ramji Venkataraman [https://arxiv.org
 /abs/1708.05932\, https://arxiv.org/abs/2008.03326\, https://arxiv.org/abs
 /2010.03460].
LOCATION:Online Event ()\, ISTA
ORGANIZER:kkhudiak@ist.ac.at
SUMMARY:Marco Mondelli: Stochastic seminar: Inference in High Dimensions fo
 r Generalized Linear Models: the Linear\, the Spectral and the Approximate
URL:https://talks-calendar.ista.ac.at/events/2974
END:VEVENT
END:VCALENDAR