9 citations · 13 across the 4 of their papers we have counts for
4 papers
Deep Learning Gaussian Processes For Computer Models with Heteroskedastic and High-Dimensional Outputs
Laura Schultz, Vadim Sokolov
Deep Learning Gaussian Processes (DL-GP) are proposed as a methodology for analyzing (approximating) computer models that produce heteroskedastic and high-dimensional output. Compu…
Bayesian Calibration for Activity Based Models
Laura Schultz, Joshua Auld, Vadim Sokolov
We consider the problem of calibration and uncertainty analysis for activity-based transportation simulators. Activity-Based Models (ABMs) rely on statistical modeling of individua…
Practical Bayesian Optimization for Transportation Simulators
Laura Schultz, Vadim Sokolov
We provide a method to solve optimization problem when objective function is a complex stochastic simulator of an urban transportation system. To reach this goal, a Bayesian optimi…
Deep Reinforcement Learning for Dynamic Urban Transportation Problems
Laura Schultz, Vadim Sokolov
We explore the use of deep learning and deep reinforcement learning for optimization problems in transportation. Many transportation system analysis tasks are formulated as an opti…