主讲人
Andrea Paudice Aarhus University 时间 2026年6月23日 星期二 下午 14:00-15:00 地点 学院104会议室
Abstract We study the problem of minimizing a convex, non-smooth Lipschitz function over a convex domain when only noisy stochastic subgradient estimates are available. We analyze the classical Stochastic Mirror Descent (SMD) algorithm and derive new tail bounds on its optimization error, for both the averaged and the last iterate. Our results extend existing analyses - traditionally limited to light-tailed, sub-Gaussian noise - to heavier-tailed noise distributions. We specialize our general bounds to two important families of noise: one with exponential tails and another with polynomial tails. Notably, our bounds for the averaged iterate reveal a distinct two-regime behavior, highlighting new insights into the interplay between noise tails and convergence rates. Biography Andrea Paudice is a tenure-track Assistant Professor in Computer Science at Aarhus University. Previously, he held a joint postdoctoral position at Italian Institute of Technology and the University of Milan (Statale), where he also obtained his PhD in Computer Science under the supervision of Nicolò Cesa-Bianchi. Before that, he spent approximately three years as a Research Fellow at Imperial College London. His research interests lie in the theory of machine learning, with a focus on stochastic optimization, generalization bounds, and the analysis of classical algorithms in non-standard settings.
