Heavy-tails and Robust Statistics in Learning Algorithms

As machine learning becomes integral to solving real-world problems, the need for robustness in learning algorithms has grown significantly. Heavy-tailed data distributions, outliers, and adversarial perturbations frequently challenge the reliability of standard machine learning methods. Addressing these challenges requires innovative approaches rooted in robust statistics and distributionally robust optimization, ensuring that models maintain performance even under challenging data conditions. This session explores recent advancements in statistical methodologies and algorithmic frameworks designed to handle heavy-tailed data and outliers. Topics include techniques for distributionally robust optimization that protect against worst-case data distributions, robust statistical methods that enhance model resilience to outliers, and the development of learning algorithms tailored to heavy-tailed and non-standard noise environments. By showcasing these cutting-edge approaches, this session highlights the pivotal role of robust statistics in advancing machine learning theory and practice.