4 papers · 1 filter
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…
Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models
Tatyana Matveeva, Aleksandr Katrutsa, Evgeny Frolov
Adaptive gradient methods like Adagrad and its variants are widespread in large-scale optimization. However, their use of diagonal preconditioning matrices limits the ability to ca…
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…
Knowledge Graph Completion with Mixed Geometry Tensor Factorization
Viacheslav Yusupov, Maxim Rakhuba, Evgeny Frolov
In this paper, we propose a new geometric approach for knowledge graph completion via low rank tensor approximation. We augment a pretrained and well-established Euclidean model ba…