5 citations · 13 across the 32 of their papers we have counts for
33 papers · 1 filter
Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulation
Aleksandr Beznosikov, Georgiy Kormakov, Alexander Grigorievskiy +7
The objective of Vertical Federated Learning (VFL) is to collectively train a model using features available on different devices while sharing the same users. This paper focuses o…
Accelerated Methods with Complexity Separation Under Data Similarity for Federated Learning Problems
Dmitry Bylinkin, Sergey Skorik, Dmitriy Bystrov +3
Heterogeneity within data distribution poses a challenge in many modern federated learning tasks. We formalize it as an optimization problem involving a computationally heavy compo…
Markovian Compression: Looking to the Past Helps Accelerate the Future
Andrey Veprikov, Vladimir Solodkin, Mikhail Rudakov +2
This paper deals with distributed optimization problems that use compressed communication to achieve efficient performance and mitigate communication bottleneck. We propose a famil…
Gradient-Free Approaches is a Key to an Efficient Interaction with Markovian Stochasticity
Boris Prokhorov, Semyon Chebykin, Alexander Gasnikov +1
This paper deals with stochastic optimization problems involving Markovian noise with a zero-order oracle. We present and analyze a novel derivative-free method for solving such pr…
Adaptive Regularized Newton Method with Inexact Hessian
Aleksandr Shestakov, Nail Bashirov, Andrei Semenov +4
Newton's method is the most widespread high-order method, demanding the gradient and the Hessian of the objective function. However, one of the main disadvantages of Newtons method…
Unified Theory of Adaptive Variance Reduction
Aleksandr Shestakov, Valery Parfenov, Aleksandr Beznosikov
Variance reduction is a family of powerful mechanisms for stochastic optimization that appears to be helpful in many machine learning tasks. It is based on estimating the exact gra…