4 citations · 4 across the 3 of their papers we have counts for
4 papers
An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine Learning
Chenxin Ma, Martin Jaggi, Frank E. Curtis +2
Distributed optimization algorithms are essential for training machine learning models on very large-scale datasets. However, they often suffer from communication bottlenecks. Conf…
Complexity Analysis of a Trust Funnel Algorithm for Equality Constrained Optimization
Frank E. Curtis, Daniel P. Robinson, Mohammadreza Samadi
A method is proposed for solving equality constrained nonlinear optimization problems involving twice continuously differentiable functions. The method employs a trust funnel appro…
Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning
Frank E. Curtis, Katya Scheinberg
The goal of this tutorial is to introduce key models, algorithms, and open questions related to the use of optimization methods for solving problems arising in machine learning. It…
A Reduced-Space Algorithm for Minimizing -Regularized Convex Functions
Tianyi Chen, Frank E. Curtis, Daniel P. Robinson
We present a new method for minimizing the sum of a differentiable convex function and an -norm regularizer. The main features of the new method include: an evolving…