BIMSA > Seminar on Control Theory and Nonlinear Filtering Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning
Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning
Organizer
Shing Toung Yau
Speaker
Yangtianze Tao
Time
Monday, August 7, 2023 3:00 PM - 3:30 PM
Venue
数学系理科楼A-203
Abstract
Stochastic Approximation (SA) is a classical algorithm that has had since the early days a huge impact on signal processing, and nowadays on machine learning, due to the necessity to deal with a large amount of data observed with uncertainties. An exemplar special case of SA pertains to the popular stochastic (sub)gradient algorithm which is the working horse behind many important applications. A lesser-known fact is that the SA scheme also extends to non-stochastic-gradient algorithms such as compressed stochastic gradient, stochastic expectation-maximization, and a number of reinforcement learning algorithms. The aim of this presentation is to overview and introduce the non-stochastic-gradient perspectives of SA to the signal processing and machine learning audiences through presenting a design guideline of SA algorithms backed by theories. Our central theme is to propose a general framework that unifies existing theories of SA, including its non-asymptotic and asymptotic convergence results, and demonstrate their applications on popular non-stochastic-gradient algorithms.