Two Approaches Towards Adaptive Optimization

Abstract:
This talk will address to recent projects I am excited about. The first describes efficient methodologies for hyper-parameter estimation in optimization algorithms. I will describe two approaches for how to adaptively estimate these parameters that often lead to significant improvement in convergence. The second describes a new method, called Metropolis-Adjusted Preconditioned Langevin Algorithm for sampling from a convex body. Taking an optimization perspective, I focus on the mixing time guarantees of these algorithms — an essential theoretical property for MCMC methods — under natural conditions over the target distribution and the geometry of the domain.

Bio:
Ashia Wilson is a Lister Brothers Career Development Assistant Professor at MIT. Her research focuses on designing scalable, reliable and socially responsible AI systems using tools from dynamical systems theory, statistics, and optimization.
She obtained her B.A. from Harvard with a concentration in applied mathematics and a minor in philosophy, and Ph.D.  from UC Berkeley in statistics. Before joining MIT, she held a postdoctoral position in the machine learning and statistics group at Microsoft Research.
Ashia has received best paper and spotlights awards for her work from conferences and workshops such as Fairness Accountability and Transparency (FAccT), Neurips, and OptML.