From the 1 of 5 linked papers with an AI index.
5 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…
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…
Beyond Convexity: Proximal-Perturbed Lagrangian Methods for Efficient Functional Constrained Optimization
Sang Bin Moon, Jong Gwang Kim, Ashish Chandra +2
Non-convex functional constrained optimization problems have gained substantial attention in machine learning and data science, addressing broad requirements that typically go beyo…
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…