26 citations · 122 across the 33 of their papers we have counts for
36 papers
Parameter-Free Zeroth-Order Optimization with Ellipsoidal Sampling
Alexander Sholokhov, Alexander Rogozin
Zeroth-order optimization methods are essential for solving black-box problems where gradient information is unavailable or expensive to compute. This paper presents POEM-ES, a nov…
Accelerated Consensus and Decentralized Optimization over Slowly Varying Networks
Darina Dvinskikh, Anton Novitskii, Demyan Yarmoshik +3
We study average consensus over slowly time-varying networks and its application to decentralized optimization. We introduce WAVE, a windowed Chebyshev method that limits the accum…
Decentralized Optimization with Cross-Coupled Mixed Affine Constraints
Ewsey Obzherin, Ilya Khomchenko, Natalia Shelegeda +4
We study decentralized optimization with cross-coupled mixed affine constraints, where local and shared variables interact through two affine channels. We show that the intrinsic d…
Decentralized Inexact Cubic Newton Method with Consensus Procedure
Artem Agafonov, Anton Novitskii, Alexander Rogozin +5
Distributed optimization is widely used in large-scale and privacy-preserving machine learning, where each agent stores a local objective and communicates only with its neighbors i…
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…
Decentralized Optimization with Mixed Affine Constraints
Demyan Yarmoshik, Nhat Trung Nguyen, Alexander Rogozin +1
This paper considers decentralized optimization of convex functions with mixed affine equality constraints involving both local and global variables. Constraints on global variable…