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DTEND:20230718T120000Z
UID:32f6c981629eaf7d3e83cf72552f7d1d-485
DTSTAMP:19700101T120011Z
DESCRIPTION:Tackling Label Corruptions: Univariate Polynomial Regression and Generalized Linear Models
URL;VALUE=URI:https://www.csa.iisc.ac.in/newweb/event/485/tackling-label-corruptions-univariate-polynomial-regression-and-generalized-linear-models/
SUMMARY:Label corruptions pose a significant challenge in various machine learning tasks, affecting the accuracy and reliability of models. In this talk, we will address two distinct problems involving label corruptions, and present approaches to handle them effectively. 

In this problem the goal is to recover a polynomial widehat p which is pointwise close to a polynomial p, given samples (x, y) where, with probability1-rho  the samples are clean, i.e. satisfy |y - p(x)| < sigma; and with probability rho  is corrupted, i.e. completely arbitrary. We propose an approach which can tolerate rho as large as any constant less than 1/2, which is the information theoretic limit for unique recovery of this problem.
 
In the second problem, we examine the challenge of learning a linear function composed with a generalized linear model, when upto 1-o(1)  (i.e. all but a vanishingly small fraction) of the labels are corrupted via arbitrary independent and additive noise. We show that in this extremely challenging setting, it is always possible to recover a polynomial-sized list of candidates, one of which is arbitrarily close to the true answer. Moreover, if a certain mild identifiability condition holds, then it is possible to prune the list and return a single such candidate.


Microsoft teams link:

https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZGE3NDg5NzktMWQ0Zi00MzFmLTg5OTgtMTMyYWM4MWQyYjI2%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%227c84465e-c38b-4d7a-9a9d-ff0dfa3638b3%22%7d


We are grateful to the Kirani family for generously supporting the theory seminar series


Hosts: Rameesh Paul, Rachana Gusain, Rahul Madhavan, KVN Sreenivas
DTSTART:20230718T120000Z
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