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Found 4 papers, 0 papers with code
Stochastic Online Linear Regression: the Forward Algorithm to Replace Ridge
We consider the problem of online linear regression in the stochastic setting.
LSTD with Random Projections
We provide a thorough theoretical analysis of the LSTD with random projections and derive performance bounds for the resulting algorithm.
Scrambled Objects for Least-Squares Regression
We consider least-squares regression using a randomly generated subspace G_P\subset F of finite dimension P, where F is a function space of infinite dimension, e. g.~L_2([0, 1]^d).
Compressed Least-Squares Regression
We consider the problem of learning, from K input data, a regression function in a function space of high dimension N using projections onto a random subspace of lower dimension M. From any linear approximation algorithm using empirical risk minimization (possibly penalized), we provide bounds on the excess risk of the estimate computed in the projected subspace (compressed domain) in terms of the excess risk of the estimate built in the high-dimensional space (initial domain).
