8 citations · 9 across the 5 of their papers we have counts for
8 papers
Stochastic Projective Splitting: Solving Saddle-Point Problems with Multiple Regularizers
Patrick R. Johnstone, Jonathan Eckstein, Thomas Flynn +1
We present a new, stochastic variant of the projective splitting (PS) family of algorithms for monotone inclusion problems. It can solve min-max and noncooperative game formulation…
A persistent adjoint method with dynamic time-scaling and an application to mass action kinetics
Thomas Flynn
In this article we consider an optimization problem where the objective function is evaluated at the fixed-point of a contraction mapping parameterized by a control variable, and o…
Bounding the expected run-time of nonconvex optimization with early stopping
Thomas Flynn, Kwang Min Yu, Abid Malik +2
This work examines the convergence of stochastic gradient-based optimization algorithms that use early stopping based on a validation function. The form of early stopping we consid…
Layered SGD: A Decentralized and Synchronous SGD Algorithm for Scalable Deep Neural Network Training
Kwangmin Yu, Thomas Flynn, Shinjae Yoo +1
Stochastic Gradient Descent (SGD) is the most popular algorithm for training deep neural networks (DNNs). As larger networks and datasets cause longer training times, training on d…
A Simultaneous Perturbation Weak Derivative Estimator for Stochastic Neural Networks
Thomas Flynn, Felisa Vázquez-Abad
In this paper we study gradient estimation for a network of nonlinear stochastic units known as the Little model. Many machine learning systems can be described as networks of homo…
Change Detection with the Kernel Cumulative Sum Algorithm
Thomas Flynn, Shinjae Yoo
Online change detection involves monitoring a stream of data for changes in the statistical properties of incoming observations. A good change detector will detect any changes shor…
The duality structure gradient descent algorithm: analysis and applications to neural networks
Thomas Flynn
The training of machine learning models is typically carried out using some form of gradient descent, often with great success. However, non-asymptotic analyses of first-order opti…
Forward sensitivity analysis for contracting stochastic systems
Thomas Flynn
In this work we investigate gradient estimation for a class of contracting stochastic systems on a continuous state space. We find conditions on the one-step transitions, namely di…