2 papers
math.OC2026
Gradient-Free Methods for Stochastic Convex Optimization with Stochastic Functional Constraints
Vadim Abronin, Alexander Gasnikov, Darina Dvinskikh
We develop accelerated gradient-free methods for stochastic convex optimization with constraints defined by expectations. Our batched primal-dual sliding method uses two-point eval…
cs.LG2024
TQCompressor: improving tensor decomposition methods in neural networks via permutations
V. Abronin, A. Naumov, D. Mazur +7
We introduce TQCompressor, a novel method for neural network model compression with improved tensor decompositions. We explore the challenges posed by the computational and storage…