BEGIN:VCALENDAR
PRODID:-//eluceo/ical//2.0/EN
VERSION:2.0
CALSCALE:GREGORIAN
BEGIN:VEVENT
UID:www.tcs.tifr.res.in/event/1431
DTSTAMP:20240627T051715Z
SUMMARY:Fair Sparse Regression with Clustering: An Invex Relaxation for a C
 ombinatorial Problem
DESCRIPTION:Speaker: Adarsh Barik (National University of Singapore (NUS))\
 n\nAbstract: \nIn this talk\, we discuss the problem of fair sparse regres
 sion on a biased dataset where bias depends upon a hidden binary attribute
 . The presence of a hidden attribute adds an extra layer of complexity to 
 the problem by combining sparse regression and clustering with unknown bin
 ary labels. The corresponding optimization problem is combinatorial\, but 
 we propose a novel reformulation of it as an invex optimization problem. W
 e show that the inclusion of the debiasing/fairness constraint in our mode
 l has no adverse effect on the performance. Rather\, it enables the recove
 ry of the hidden attribute. The support of our recovered regression parame
 ter vector matches exactly with the true parameter vector. Moreover\, we s
 imultaneously solve the clustering problem by recovering the exact value o
 f the hidden attribute for each sample. Our method uses carefully construc
 ted primal-dual witnesses to provide theoretical guarantees for the combin
 atorial problem. We show that the sample complexity of our method is logar
 ithmic in terms of the dimension of the regression parameter vector.\nShor
 t Bio:\nAdarsh Barik is a postdoc at the Institute of Data Science at the 
 National University of Singapore. He did his PhD in Computer Science at Pu
 rdue University and his undergrad at IIT Madras before that. His research 
 interests are broadly in theoretical and computational aspects of optimiza
 tion\, machine learning\, information theory\, and high-dimensional data a
 nalysis.\n
URL:https://www.tcs.tifr.res.in/web/events/1431
DTSTART;TZID=Asia/Kolkata:20240701T160000
DTEND;TZID=Asia/Kolkata:20240701T170000
LOCATION:A-201 (STCS Seminar Room)
END:VEVENT
END:VCALENDAR