公开(公告)号：US20220108219A1

公开(公告)日：2022-04-07

申请号：US17492046

申请日：2021-10-01

Applicant: Google LLC

Inventor： Gil Shamir ,  Wojciech Szpankowski

IPC: G06N20/00 ,  G06N7/02

Abstract: Systems and methods leverage low complexity (e.g., linear overall, fixed per example) analytical approximations to perform machine learning problems such as, for example, the sparse online logistic regression problem. Unlike variational inference and other methods, the proposed systems and methods lead to analytical closed forms, lowering the practical number of computations. Further, unlike techniques used for dense features sets, such as Gaussian Mixtures, the proposed systems and methods allow for sparse problems with huge feature sets without increasing complexity. With the analytical closed forms, there is also no need for applying stochastic gradient methods on surrogate losses, and for tuning and balancing learning and regularization parameters of such methods.