From the 1 of 7 linked papers with an AI index.
7 papers
First-Order Softmax Weighted Switching Gradient Method for Distributed Stochastic Minimax Optimization with Stochastic Constraints
Zhankun Luo, Antesh Upadhyay, Sang Bin Moon +1
The paper introduces a first-order Softmax‑Weighted Switching Gradient algorithm for distributed stochastic minimax optimization with stochastic constraints, offering theoretical g…
Unified High-Probability Analysis of Stochastic Variance-Reduced Estimation
Zhankun Luo, Antesh Upadhyay, M. Berk Sahin +3
Stochastic estimators are fundamental to large-scale optimization, where population quantities must be inferred from noisy oracle observations. Although influential methods such as…
Beyond Bounded Variance: Variance-Reduced Normalized Methods for Nonconvex Optimization under Blum-Gladyshev Noise
Antesh Upadhyay, Arda Fazla, Abolfazl Hashemi
We study nonconvex stochastic optimization under the Blum-Gladyshev (-0) noise model, where the stochastic gradient variance grows quadratically with the distance from…
Lower Bounds and Proximally Anchored SGD for Non-Convex Minimization Under Unbounded Variance
Arda Fazla, Ege C. Kaya, Antesh Upadhyay +1
Analysis of Stochastic Gradient Descent (SGD) and its variants typically relies on the assumption of uniformly bounded variance, a condition that frequently fails in practical non-…
FedSGM: A Unified Framework for Constraint Aware, Bidirectionally Compressed, Multi-Step Federated Optimization
Antesh Upadhyay, Sang Bin Moon, Abolfazl Hashemi
We introduce FedSGM, a unified framework for federated constrained optimization that addresses four major challenges in federated learning (FL): functional constraints, communicati…
Optimization via First-Order Switching Methods: Skew-Symmetric Dynamics and Optimistic Discretization
Antesh Upadhyay, Sang Bin Moon, Abolfazl Hashemi
Large-scale constrained optimization problems are at the core of many tasks in control, signal processing, and machine learning. Notably, problems with functional constraints arise…