5 papers
Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning
Chia-Yuan Wu, Frank E. Curtis, Daniel P. Robinson
Collaborative machine learning (CML) enables multiple clients to train a global model jointly in a data-distributed setting. To address data privacy and communication efficiency, o…
Low-Order Explicit Hessian Imitation Method for Large-Scale Supervised Machine Learning
Yunlang Zhu, Lingjun Guo, Zahra Khatti +4
An algorithm is proposed for solving optimization problems arising in neural network training for supervised learning. The unique feature of the algorithm is the use of an auxiliar…
A Gradient Sampling Algorithm for Noisy Nonsmooth Nonconvex Optimization
Albert S. Berahas, Frank E. Curtis, Lara Zebiane
An algorithm is proposed, analyzed, and tested for minimizing locally Lipschitz objective functions that may be nonconvex and/or nonsmooth. The algorithm, which is built upon the g…
Projected Stochastic Momentum Methods for Nonlinear Equality-Constrained Optimization for Machine Learning
Qi Wang, Christian Piermarini, Yunlang Zhu +1
Two algorithms are proposed, analyzed, and tested for solving continuous optimization problems with nonlinear equality constraints. Each is an extension of a stochastic momentum-ba…
Gradient Sampling Methods with Inexact Subproblem Solutions and Gradient Aggregation
Frank E. Curtis, Minhan Li
Gradient sampling (GS) has proved to be an effective methodology for the minimization of objective functions that may be nonconvex and/or nonsmooth. The most computationally expens…