26 citations · 34 across the 12 of their papers we have counts for
13 papers
High-Dimensional Regularization of the Spatial Sign Covariance Matrix for Robust Shape Estimation
Jonas Elmerraji, James C. Spall, Mateo Díaz
Covariance estimation is a key component of many applications in system identification and data-driven control. Although heavy-tailed distributions may lack a covariance matrix to…
Zeroth-Order Langevin Monte Carlo via SPSA under Noisy Function Measurements
Hongbo Li, James C. Spall
In sampling problems, gradient-based schemes such as Langevin Monte Carlo (LMC) mix faster than non-gradient-based methods, but their applicability is limited by access to the grad…
Autocovariance and Optimal Design for Random Walk Metropolis-Hastings Algorithm
Jingyi Zhang, James C. Spall
The Metropolis-Hastings algorithm has been extensively studied in the estimation and simulation literature, with most prior work focusing on convergence behavior and asymptotic the…
Simulation-Based Optimization for Policy Incentives and Planning of Hybrid Microgrids
Nanrui Gong, James C. Spall
Transitioning to renewable power generation is often difficult for remote or isolated communities, due to generation intermittency and high cost barriers. Our paper presents a simu…
Difference Between Cyclic and Distributed Approach in Stochastic Optimization for Multi-agent System
Jiahao Shi, James C. Spall
Many stochastic optimization problems in multi-agent systems can be decomposed into smaller subproblems or reduced decision subspaces. The cyclic and distributed approaches are two…
Training neural networks with end-to-end optical backpropagation
James Spall, Xianxin Guo, A. I. Lvovsky
Optics is an exciting route for the next generation of computing hardware for machine learning, promising several orders of magnitude enhancement in both computational speed and en…
Switch Updating in SPSA Algorithm for Stochastic Optimization with Inequality Constraints
Zhichao Jia, Ziyi Wei, James C. Spall
Simultaneous perturbation stochastic approximation (SPSA) is widely used in stochastic optimization due to its high efficiency, asymptotic stability, and reduced number of required…
Discrete Stochastic Optimization for Public Health Interventions with Constraints
Zewei Li, James C. Spall
Many public health threats exist, motivating the need to find optimal intervention strategies. Given the stochastic nature of the threats (e.g., the spread of pandemic influenza, t…
Hybrid training of optical neural networks
James Spall, Xianxin Guo, A. I. Lvovsky
Optical neural networks are emerging as a promising type of machine learning hardware capable of energy-efficient, parallel computation. Today's optical neural networks are mainly…
Efficient Implementation of Second-Order Stochastic Approximation Algorithms in High-Dimensional Problems
Jingyi Zhu, Long Wang, James C. Spall
Stochastic approximation (SA) algorithms have been widely applied in minimization problems when the loss functions and/or the gradient information are only accessible through noisy…
Error Analysis for the Particle Filter: Methods and Theoretical Support
Ziyu Liu, Shihong Wei, James C. Spall
The particle filter is a popular Bayesian filtering algorithm for use in cases where the state-space model is nonlinear and/or the random terms (initial state or noises) are non-Ga…
Modeling Traffic Networks Using Integrated Route and Link Data
Xilei Zhao, James C. Spall
Real-time navigation services, such as Google Maps and Waze, are widely used in daily life. These services provide rich data resources in real-time traffic conditions and travel ti…
Relative Performance of Expected and Observed Fisher Information in Covariance Estimation for Maximum Likelihood Estimates
Xumeng Cao
Maximum likelihood estimation is a popular method in statistical inference. As a way of assessing the accuracy of the maximum likelihood estimate (MLE), the calculation of the cova…