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cs.LG2026
Scalable LinUCB: Low-Rank Design Matrix Updates for Recommenders with Large Action Spaces
Evgenia Shustova, Marina Sheshukova, Sergey Samsonov +1
In this paper, we introduce PSI-LinUCB, a scalable variant of LinUCB that enables efficient training, inference, and memory usage by representing the inverse regularized design mat…
cs.LG2026
Matrix-Free Two-to-Infinity and One-to-Two Norms Estimation
Askar Tsyganov, Evgeny Frolov, Sergey Samsonov +1
In this paper, we propose new randomized algorithms for estimating the two-to-infinity and one-to-two norms in a matrix-free setting, using only matrix-vector multiplications. Our…