28 citations · 34 across the 12 of their papers we have counts for
20 papers
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…
CaCuTe Meets SESOP: Better Theory and Practice with Lipschitz Hessians
Nazarii Tupitsa
We combine CaCuTe with sequential subspace optimization (SESOP) for smooth convex minimization under Lipschitz continuity of the Hessian. The resulting method, CaCuSESOP, achieves…
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…
Lower Bounds For Gradient-Free Convex Optimization And Convex-Concave Saddle-Point Problems
Yuriy Dorn, Darina Dvinskikh, Timofei Loginov +4
We study the query complexity of optimization with exact scalar-value information. For globally -smooth convex functions on with a minimizer in a Euclidean ball of…
The Complexity of Convex Optimization with Mismatched Geometry
Timofei Loginov, Alexander Gasnikov, Yuriy Dorn +4
Optimal first-order methods on non-Euclidean domains such as the ball pair the prox-function with the norm in which smoothness…
Stochastic Optimization and Data Science
Arutyun Avetisyan, Darina Dvinskikh, Alexander Gasnikov +3
This paper aims to motivate stochastic optimization problems from a statistical perspective and a statistical learning perspective, where the goal is to maximize the log-likelihood…
CaCuTe: Casual Cubic-Model Technique for Faster Optimization
Nazarii Tupitsa
We establish a local rate for the gradient update under a -Hessian--Lipschitz assumption. Regime det…
On Solving Minimization and Min-Max Problems by First-Order Methods with Relative Error in Gradients
Artem Vasin, Valery Krivchenko, Dmitry Kovalev +6
First-order methods for minimization and saddle point (min-max) problems are widely used for solving large-scale problems, in particular arising in machine learning. The majority o…
Methods for Convex -Smooth Optimization: Clipping, Acceleration, and Adaptivity
Eduard Gorbunov, Nazarii Tupitsa, Sayantan Choudhury +4
Due to the non-smoothness of optimization problems in Machine Learning, generalized smoothness assumptions have been gaining a lot of attention in recent years. One of the most pop…
Low-Resource Machine Translation through the Lens of Personalized Federated Learning
Viktor Moskvoretskii, Nazarii Tupitsa, Chris Biemann +3
We present a new approach called MeritOpt based on the Personalized Federated Learning algorithm MeritFed that can be applied to Natural Language Tasks with heterogeneous data. We…
Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad
Sayantan Choudhury, Nazarii Tupitsa, Nicolas Loizou +3
Adaptive methods are extremely popular in machine learning as they make learning rate tuning less expensive. This paper introduces a novel optimization algorithm named KATE, which…
Federated Learning Can Find Friends That Are Advantageous
Nazarii Tupitsa, Samuel Horváth, Martin Takáč +1
In Federated Learning (FL), the distributed nature and heterogeneity of client data present both opportunities and challenges. While collaboration among clients can significantly e…
Byzantine-Tolerant Methods for Distributed Variational Inequalities
Nazarii Tupitsa, Abdulla Jasem Almansoori, Yanlin Wu +4
Robustness to Byzantine attacks is a necessity for various distributed training scenarios. When the training reduces to the process of solving a minimization problem, Byzantine rob…
Primal-Dual Gradient Methods for Searching Network Equilibria in Combined Models with Nested Choice Structure and Capacity Constraints
Meruza Kubentayeva, Demyan Yarmoshik, Mikhail Persiianov +8
We consider a network equilibrium model (i.e. a combined model), which was proposed as an alternative to the classic four-step approach for travel forecasting in transportation net…
Numerical Methods for Large-Scale Optimal Transport
Nazarii Tupitsa, Pavel Dvurechensky, Darina Dvinskikh +1
The optimal transport (OT) problem is a classical optimization problem having the form of linear programming. Machine learning applications put forward new computational challenges…
Accelerated Alternating Minimization and Adaptability to Strong Convexity
Nazarii Tupitsa
In the first part of the paper we consider accelerated first order optimization method for convex functions with -Lipschitz-continuous gradient, that is able to automatically ad…
Multimarginal Optimal Transport by Accelerated Alternating Minimization
Nazarii Tupitsa, Pavel Dvurechensky, Alexander Gasnikov +1
We consider a multimarginal optimal transport, which includes as a particular case the Wasserstein barycenter problem. In this problem one has to find an optimal coupling between $…
Alternating Minimization Methods for Strongly Convex Optimization
Nazarii Tupitsa, Pavel Dvurechensky, Alexander Gasnikov +1
{We consider alternating minimization procedures for convex optimization problems with variable divided in many block, each block being amenable for minimization with respect to it…
On a Combination of Alternating Minimization and Nesterov's Momentum
Sergey Guminov, Pavel Dvurechensky, Nazarii Tupitsa +1
Alternating minimization (AM) procedures are practically efficient in many applications for solving convex and non-convex optimization problems. On the other hand, Nesterov's accel…
On the Complexity of Approximating Wasserstein Barycenter
Alexey Kroshnin, Darina Dvinskikh, Pavel Dvurechensky +3
We study the complexity of approximating Wassertein barycenter of discrete measures, or histograms of size by contrasting two alternative approaches, both using entropic re…