The focus of this workshop will be on recent developments in randomized linear algebra, with an emphasis on how algorithmic improvements from the theory of algorithms interact with statistical, optimization, inference, and related perspectives. One focus area of the workshop will be the broad use of sketching techniques developed in the data stream literature for solving optimization problems in linear and multi-linear algebra. The workshop will also consider the impact of theoretical developments in randomized linear algebra on (i) numerical analysis as a method for constructing preconditioners; (ii) applications as a principled feature selection method; and (iii) implementations as a way to avoid communication rather than computation. Another goal of this workshop is thus to bridge the theory-practice gap by trying to understand the needs of practitioners when working on real datasets.
Further details about this workshop will be posted in due course.
Haim Avron (Tel Aviv University), Laura Balzano (University of Michigan), Ken Clarkson (IBM Almaden), Michal Derezinski (UC Santa Cruz), Petros Drineas (Purdue University), Mark Embree (Virginia Tech), Matan Gavish (Hebrew University of Jerusalem), Alex Gittens (Rensselaer Polytechnic Institute), David Gleich (Purdue University), Ilse Ipsen (North Carolina State University), Prateek Jain (Microsoft Research India), Yiannis Koutis (New Jersey Institute of Technology), Rasmus Kyng (Harvard University), Lek-heng Lim (University of Chicago), Ping Ma (University of Georgia), Michael Mahoney (International Computer Science Institute and UC Berkeley), Gunnar Martinsson (University of Texas at Austin), Shannon McCurdy (UC Berkeley), Cameron Musco (Microsoft Research New England), Christopher Musco (MIT), Praneeth Netrapalli (Microsoft Research India), Huy Nguyen (Northeastern University), Richard Peng (Georgia Institute of Technology), Peter Richtarik (University of Edinburgh), Fred Roosta (University of Queensland), Uros Seljak (UC Berkeley)