1 citations · 1 across the 13 of their papers we have counts for
13 papers
Non-Convex Federated Optimization under Cost-Aware Client Selection
Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich
Different federated optimization algorithms typically employ distinct client-selection strategies: some methods communicate only with a randomly sampled subset of clients at each r…
Composite Optimization with Error Feedback: the Dual Averaging Approach
Yuan Gao, Anton Rodomanov, Jeremy Rack +1
Communication efficiency is a central challenge in distributed machine learning training, and message compression is a widely used solution. However, standard Error Feedback (EF) m…
Exploiting Similarity for Computation and Communication-Efficient Decentralized Optimization
Yuki Takezawa, Xiaowen Jiang, Anton Rodomanov +1
Reducing communication complexity is critical for efficient decentralized optimization. The proximal decentralized optimization (PDO) framework is particularly appealing, as method…
Accelerated Distributed Optimization with Compression and Error Feedback
Yuan Gao, Anton Rodomanov, Jeremy Rack +1
Modern machine learning tasks often involve massive datasets and models, necessitating distributed optimization algorithms with reduced communication overhead. Communication compre…
Decoupled SGDA for Games with Intermittent Strategy Communication
Ali Zindari, Parham Yazdkhasti, Anton Rodomanov +2
We focus on reducing communication overhead in multiplayer games, where frequently exchanging strategies between players is not feasible and players have noisy or outdated strategi…
DADA: Dual Averaging with Distance Adaptation
Mohammad Moshtaghifar, Anton Rodomanov, Daniil Vankov +1
We present a novel universal gradient method for solving convex optimization problems. Our algorithm, Dual Averaging with Distance Adaptation (DADA), is based on the classical sche…
Optimizing -Smooth Functions by Gradient Methods
Daniil Vankov, Anton Rodomanov, Angelia Nedich +2
We study gradient methods for optimizing -smooth functions, a class that generalizes Lipschitz-smooth functions and has gained attention for its relevance in machine le…
Stabilized Proximal-Point Methods for Federated Optimization
Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich
In developing efficient optimization algorithms, it is crucial to account for communication constraints -- a significant challenge in modern Federated Learning. The best-known comm…
Universality of AdaGrad Stepsizes for Stochastic Optimization: Inexact Oracle, Acceleration and Variance Reduction
Anton Rodomanov, Xiaowen Jiang, Sebastian Stich
We present adaptive gradient methods (both basic and accelerated) for solving convex composite optimization problems in which the main part is approximately smooth (a.k.a. …
Federated Optimization with Doubly Regularized Drift Correction
Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich
Federated learning is a distributed optimization paradigm that allows training machine learning models across decentralized devices while keeping the data localized. The standard m…
Non-convex Stochastic Composite Optimization with Polyak Momentum
Yuan Gao, Anton Rodomanov, Sebastian U. Stich
The stochastic proximal gradient method is a powerful generalization of the widely used stochastic gradient descent (SGD) method and has found numerous applications in Machine Lear…
Universal Gradient Methods for Stochastic Convex Optimization
Anton Rodomanov, Ali Kavis, Yongtao Wu +2
We develop universal gradient methods for Stochastic Convex Optimization (SCO). Our algorithms automatically adapt not only to the oracle's noise but also to the Hölder smoothness…
Polynomial Preconditioning for Gradient Methods
Nikita Doikov, Anton Rodomanov
We study first-order methods with preconditioning for solving structured nonlinear convex optimization problems. We propose a new family of preconditioners generated by symmetric p…