Robust Regression via Model Based Methods

TL;DR

This paper introduces a new model-based optimization algorithm for robust regression that effectively handles outliers and demonstrates superior efficiency over traditional gradient methods in various applications.

Contribution

It develops SADM, a stochastic algorithm for robust regression based on model-based optimization, improving robustness and convergence in outlier-prone data.

Findings

SADM converges at a rate of O(log T/T)

l_p norms are robust to outliers

Model-based algorithms outperform gradient methods in experiments

Abstract

The mean squared error loss is widely used in many applications, including auto-encoders, multi-target regression, and matrix factorization, to name a few. Despite computational advantages due to its differentiability, it is not robust to outliers. In contrast, l_p norms are known to be robust, but cannot be optimized via, e.g., stochastic gradient descent, as they are non-differentiable. We propose an algorithm inspired by so-called model-based optimization (MBO) [35, 36], which replaces a non-convex objective with a convex model function and alternates between optimizing the model function and updating the solution. We apply this to robust regression, proposing SADM, a stochastic variant of the Online Alternating Direction Method of Multipliers (OADM) [50] to solve the inner optimization in MBO. We show that SADM converges with the rate O(log T/T). Finally, we demonstrate experimentally (a) the robustness of l_p norms to outliers and (b) the efficiency of our proposed model-based algorithms in comparison with gradient methods on autoencoders and multi-target regression.