4 citations · 11 across the 10 of their papers we have counts for
10 papers
Probabilistic Models for Manufacturing Lead Times
Recep Yusuf Bekci, Yacine Mahdid, Jinling Xing +3
In this study, we utilize Gaussian processes, probabilistic neural network, natural gradient boosting, and quantile regression augmented gradient boosting to model lead times of la…
Sparse Representations of Solutions to a class of Random Boundary Value Problems
Fang Yang, Min Chen, Jiecheng Chen +2
We introduce certain sparse representation methods, named as stochastic pre-orthogonal adaptive Fourier decomposition 1 and 2 (SPOAFD1 and SPOAFD2) to solve the Dirichlet boundary…
Building an AI-ready RSE Workforce
Ying Zhang, Matthew A. Gitzendanner, Dan S. Maxwell +6
Artificial Intelligence has been transforming industries and academic research across the globe, and research software development is no exception. Machine learning and deep learni…
Non-asymptotic estimates for TUSLA algorithm for non-convex learning with applications to neural networks with ReLU activation function
Dong-Young Lim, Ariel Neufeld, Sotirios Sabanis +1
We consider non-convex stochastic optimization problems where the objective functions have super-linearly growing and discontinuous stochastic gradients. In such a setting, we prov…
Revisiting Time, Clocks, and Synchronization
Ying Zhang
Sub-nanosecond precision clock synchronization over the packet network has been achieved by the White Rabbit protocol for a decade. However, few computer systems utilize such a tec…
Data-Driven Vulnerability Detection and Repair in Java Code
Ying Zhang, Mahir Kabir, Ya Xiao +3
Java platform provides various APIs to facilitate secure coding. However, correctly using security APIs is usually challenging for developers who lack cybersecurity training. Prior…
A fully data-driven approach to minimizing CVaR for portfolio of assets via SGLD with discontinuous updating
Sotirios Sabanis, Ying Zhang
A new approach in stochastic optimization via the use of stochastic gradient Langevin dynamics (SGLD) algorithms, which is a variant of stochastic gradient decent (SGD) methods, al…
Fully Distributed and Asynchronized Stochastic Gradient Descent for Networked Systems
Ying Zhang
This paper considers a general data-fitting problem over a networked system, in which many computing nodes are connected by an undirected graph. This kind of problem can find many…
Restricted Low-Rank Approximation via ADMM
Ying Zhang
The matrix low-rank approximation problem with additional convex constraints can find many applications and has been extensively studied before. However, this problem is shown to b…
A Simple and General Problem and its Optimal Randomized Online Algorithm Design with Competitive Analysis
Ying Zhang
The online algorithm design was proposed to handle the caching problem when the future information is unknown. And currently, it draws more and more attentions from the researchers…