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Yu Lu (CREST-ENSAE) “Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance”

October 10 @ 2:00 pm - 3:00 pm

Statistical Seminar: Every Monday.
Time: 2:00 pm
Date: 10 of October 2022
Place: Room 3001

Yu Lu (CREST-ENSAE) “Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance”

Abstract:We study stochastic convex optimization under infinite noise variance. Specifically, when the stochastic gradient is unbiased and has uniformly bounded (1 + κ)-th moment, for some κ ∈ (0, 1], we quantify the convergence rate of the Stochastic Mirror Descent algorithm with a particular class of uniformly convex mirror maps, in terms of the number of iterations, dimensionality and related geometric parameters of the optimization problem. Interestingly this algorithm does not require any explicit gradient clipping or normalization, which have been extensively used in several recent empirical and theoretical works. We complement our convergence results with information-theoretic lower bounds showing that no other algorithm using only stochastic first-order oracles can achieve improved rates. Our results have several interesting consequences for devising online/streaming stochastic approximation algorithms for problems arising in robust statistics and machine learning.

 

Organizers:
Cristina BUTUCEA (CREST), Alexandre TSYBAKOV (CREST), Karim LOUNICI (CMAP) , Jaouad MOURTADA (CREST)
Sponsors:
CREST-CMAP