23.06.2024

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MCML Researchers With One Paper at COLT 2024

37th Annual Conference on Learning Theory (COLT 2024). Edmonton, Canada, 30.06.2024–03.07.2024

We are happy to announce that MCML researchers are represented with one paper at COLT 2024. Congrats to our researchers!

Main Track (1 papers)

C. M. Verdun, O. Melnyk, F. Krahmer and P. Jung.
Fast, blind, and accurate: Tuning-free sparse regression with global linear convergence.
COLT 2024 - 37th Annual Conference on Learning Theory. Edmonton, Canada, Jun 30-Jul 03, 2024. URL
Abstract

Many algorithms for high-dimensional regression problems require the calibration of regularization hyperparameters. This, in turn, often requires the knowledge of the unknown noise variance in order to produce meaningful solutions. Recent works show, however, that there exist certain estimators that are pivotal, i.e., the regularization parameter does not depend on the noise level; the most remarkable example being the square-root lasso. Such estimators have also been shown to exhibit strong connections to distributionally robust optimization. Despite the progress in the design of pivotal estimators, the resulting minimization problem is challenging as both the loss function and the regularization term are non-smooth. To date, the design of fast, robust, and scalable algorithms with strong convergence rate guarantees is still an open problem. This work addresses this problem by showing that an iteratively reweighted least squares (IRLS) algorithm exhibits global linear convergence under the weakest assumption available in the literature. We expect our findings will also have implications for multi-task learning and distributionally robust optimization.

MCML Authors
Claudio Mayrink Verdun

Claudio Mayrink Verdun

Dr.

* Former Member

Link to Profile Felix Krahmer

Felix Krahmer

Prof. Dr.

Optimization & Data Analysis


23.06.2024


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