3 citations · 10 across the 19 of their papers we have counts for
9 papers · 1 filter
Apertus 1.5: From Open Text Models to Open Multimodal Reasoners
Project Apertus, :, Yixuan Xu +130
Language models are becoming infrastructure, and infrastructure built in the open, by public institutions, on documented data, is the alternative to dependence on a few providers;…
Adaptive two-points zero-order accelerated methods for stochastic convex optimization problems
Yuriy Dorn, Osman Osmanov, Timofei Loginov +3
We study stochastic convex optimization, where each sampled function can be evaluated at two arbitrary points. Our accelerated method chooses step sizes, batch sizes and smoothing…
Optimal Rates for Federated Variational Inequalities under different relaxations of bounded variance assumption
Kirill Novoselov, Yuriy Dorn, Darina Dvinskikh +1
We study how interpolation affects local steps and parallel sampling in monotone variational inequalities (VIs), with workers, oracle calls per worker per round, and ro…
Minimax Oracle Call Complexity, Rounds and Admissible Noise in Zeroth-Order Optimization of Highly Smooth Functions
Timofei Loginov, Darina Dvinskikh, Nazarii Tupitsa +3
We study zeroth-order optimization of convex and strongly convex functions with Hölder smoothness of order from values corrupted by random noise with bias independent of the…
Asking the Crowd the Right Question: Bias-Cancelling Weights for Federated Learning
Ilya Kuruzov, Dmitrii Vishovan, Kirill Novoselov +3
A federated objective is a weighted sum of client risks, and the weights are almost always fixed in advance. We treat them instead as the only instrument of a wisdom-of-crowds mech…
Accelerated Stochastic Method under -Smoothness and Heavy-Tailed Noise
Aleksandr Lobanov, Darina Dvinskikh, Alexander Gasnikov
We develop an accelerated stochastic method for convex -smooth optimization under heavy-tailed noise. The unbiased oracle has a finite -th noise moment, , wi…
Optimal Parameter-Free Second-order Acceleration with Inexact Hessians under Generalized Smoothness
Artem Agafonov, Aslan Ashabokov, Alexander D'yakonov +3
We study second-order convex optimization under generalized Hessian smoothness, where the local Hessian Lipschitz constant may grow linearly with the gradient norm. For inexact Hes…
Smooth or Separable? Sparse Block Acceleration for Entropy-Regularized Linear Programs
Irina Podlipnova, Maxim Mashtaler, Artem Agafonov +2
We study entropy-regularized linear programs with sparse affine constraints and compare two exact dual representations induced by whether a redundant normalization constraint is re…
Localize, Restart, Accelerate: Stochastic Optimization under Generalized Smoothness
Darina Dvinskikh, Alexander Gasnikov, Aleksandr Lobanov +1
We study stochastic convex optimization under asymmetric \((L_0,L_1)\)-generalized smoothness, a model motivated by machine-learning objectives whose local curvature may grow with…