2 citations · 3 across the 13 of their papers we have counts for
15 papers
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;…
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…
Robust Decentralized Optimization under Node Failures via Adaptive Regularization
Ilya Kuruzov, Anastasiia Murzina
We study decentralized minimization of a sum of functions over a network where nodes may only leave, the remaining nodes stay connected, and the topology freezes between departures…
A Line-search-free Method for Adaptive Decentralized Optimization
Xiaokai Chen, Ilya Kuruzov, Gesualdo Scutari
We study decentralized optimization over networks where agents cooperatively minimize a smooth (strongly) convex sum of local losses while communicating only with immediate neighbo…
Adaptive Decentralized Composite Optimization via Three-Operator Splitting
Xiaokai Chen, Ilya Kuruzov, Gesualdo Scutari
The paper studies decentralized optimization over networks, where agents minimize a sum of {\it locally} smooth (strongly) convex losses and plus a nonsmooth convex extended value…
A Parameter-free Decentralized Algorithm for Composite Convex Optimization
Xiaokai Chen, Ilya Kuruzov, Gesualdo Scutari +1
The paper studies decentralized optimization over networks, where agents minimize a composite objective consisting of the sum of smooth convex functions--the agents' losses--and an…
Adaptive Stepsize Selection in Decentralized Convex Optimization
Ilya Kuruzov, Xiaokai Chen, Gesualdo Scutari +1
We study decentralized optimization where multiple agents minimize the average of their (strongly) convex, smooth losses over a communication graph. Convergence of the existing dec…
Mixed Newton Method for Optimization in Complex Spaces
Nikita Yudin, Roland Hildebrand, Sergey Bakhurin +5
In this paper, we modify and apply the recently introduced Mixed Newton Method, which is originally designed for minimizing real-valued functions of complex variables, to the minim…
On Some Versions of Subspace Optimization Methods with Inexact Gradient Information
Ilya Kuruzov, Fedor Stonyakin
It is well-known that accelerated gradient methods possess optimal complexity estimates for the class of convex smooth minimization problems. In many practical situations, it makes…
Gradient-Type Methods for Optimization Problems with Polyak-Łojasiewicz Condition: Early Stopping and Adaptivity to Inexactness Parameter
Ilya A. Kuruzov, Fedor S. Stonyakin, Mohammad S. Alkousa
Due to its applications in many different places in machine learning and other connected engineering applications, the problem of minimization of a smooth function that satisfies t…
The Mirror-Prox Sliding Method for Non-smooth decentralized saddle-point problems
Ilya Kuruzov, Alexander Rogozin, Demyan Yarmoshik +1
The saddle-point optimization problems have a lot of practical applications. This paper focuses on such non-smooth problems in decentralized case. This work contains generalization…
Gradient-Type Methods For Decentralized Optimization Problems With Polyak-Łojasiewicz Condition Over Time-Varying Networks
Ilya Kuruzov, Mohammad Alkousa, Fedor Stonyakin +1
This paper focuses on the decentralized optimization (minimization and saddle point) problems with objective functions that satisfy Polyak-Łojasiewicz condition (PL-condition). The…
Sequential Subspace Optimization for Quasar-Convex Optimization Problems with Inexact Gradient
Ilya Kuruzov, Fedor Stonyakin
It is well-known that accelerated gradient first-order methods possess optimal complexity estimates for the class of convex smooth minimization problems. In many practical situatio…
Solving strongly convex-concave composite saddle point problems with a small dimension of one of the variables
Egor Gladin, Ilya Kuruzov, Fedor Stonyakin +3
The article is devoted to the development of algorithmic methods ensuring efficient complexity bounds for strongly convex-concave saddle point problems in the case when one of the…
Accelerated Methods for -Weakly-Quasi-Convex Problems
Sergey Guminov, Alexander Gasnikov, Ilya Kuruzov
We provide a quick overview of the class of -weakly-quasi-convex problems and its relationships with other problem classes. We show that the previously known Sequential Subspace…