Optimal Subspace Estimation with Noise


At this 5-minute lightning talk at the Discovery Institute, I will talk about my work on a project on approximating incomplete data with varieties. If there’s some unknown data that we think has linear structure and we only have access to noisy low dimensional projections (think - an unknown (linear) object in a dark room and I only show you the shadows after we shine a light), how accurately can we reconstruct the original unknown data? We derived an upper bound to this in our paper: a perturbation bound for the optimal subspace estimator from canonical projections

Jul 20, 2022 1:15 PM — 1:20 PM
Madison, Wisconsin

You can find my slides here and the original paper here.

Karan Srivastava
Karan Srivastava
PhD Student, Mathematics

My research interests include machine learning, reinforcement learning, combinatorics, and algebraic geometry