Recorded 29 November 2022. Piotr Indyk of the Massachusetts Institute of Technology presents "Learning-Based Low-Rank Approximations" at IPAM's Multi-Modal Imaging with Deep Learning and Modeling Workshop. Abstract: I will give an overview of the recent line of work on learning-based algorithms for the low-rank approximation problem. Such algorithms use training sets of input matrices in order to optimize their performance. Specifically, some of the most efficient approximate algorithms for computing low-rank approximations to a matrix A follow the following two step process: first compute a “sketch” SA, where S is a random m×n "sketching matrix", and then perform the singular value decomposition of SA. Their learning-based versions replace the random matrix S with a "learned" matrix, which results in significant reduction of the approximation error. Joint work with Peter Bartlett, Yang Yuan, Ali Vakilian, Tal Wagner, David Woodruff Learn more online at: http://www.ipam.ucla.edu/programs/workshops/workshop-iv-multi-modal-imaging-with-deep-learning-and-modeling/?tab=overview