5 papers
Fast Differentiable SVD on GPU via Polar Decomposition
Uliana Parkina, Askar Tsyganov, Sergei Kudriashov +2
We present a fully GPU-oriented SVD pipeline based on polar decomposition, motivated by iterative methods that rely solely on matrix multiplications, such as the Newton-Schulz iter…
A lower bound for matrix multiplication
Askar Tsyganov, Uliana Parkina, Sergey Samsonov +1
We prove that, over any field, the bilinear complexity of multiplying a matrix by a matrix is strictly greater than . In particular, every exact bili…
Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies
Ekaterina Grishina, Stepan Kuznetsov, Askar Tsyganov +8
The ranking of recommendation algorithms is a challenging problem since model performance is sensitive to dataset characteristics such as sparsity, sequential structure, and scale.…
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…
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…