1k citations · 1.3k across the 26 of their papers we have counts for
32 papers
Generative Causal Inference
Maria Nareklishvili, Nicholas Polson, Vadim Sokolov
Generative Bayesian Computation (GBC) methods are developed for Casual Inference. Generative methods are simulation-based methods that use a large training dataset to represent pos…
Generative AI for Bayesian Computation
Nicholas G. Polson, Vadim Sokolov
Bayesian Generative AI (BayesGen-AI) methods are developed and applied to Bayesian computation. BayesGen-AI reconstructs the posterior distribution by directly modeling the paramet…
Feature Selection for Personalized Policy Analysis
Maria Nareklishvili, Nicholas Polson, Vadim Sokolov
In this paper, we propose Forest-PLS, a feature selection method for analyzing policy effect heterogeneity in a more flexible and comprehensive manner than is typically available w…
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…
Quantum Bayesian Computation
Nick Polson, Vadim Sokolov, Jianeng Xu
Quantum Bayesian Computation (QBC) is an emerging field that levers the computational gains available from quantum computers to provide an exponential speed-up in Bayesian computat…
Deep Partial Least Squares for Instrumental Variable Regression
Maria Nareklishvili, Nicholas Polson, Vadim Sokolov
In this paper, we propose deep partial least squares for the estimation of high-dimensional nonlinear instrumental variable regression. As a precursor to a flexible deep neural net…
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…
Housing Market Forecasting using Home Showing Events
Yuanyuan Zha, Susan T. Parker, James J. Foster +1
Both buyers and sellers face uncertainty in real estate transactions in about when to time a transaction and at what cost. Both buyers and sellers make decisions without knowing th…
Bayesian Learning: A Selective Overview
Yu Lin Hsu, Chu Chuan Jeng, Pavithra Sripathanallur Murali +4
This paper presents an overview of some of the concepts of Bayesian Learning. The number of scientific and industrial applications of Bayesian learning has been growing in size rap…
Deep Generative Models for Vehicle Speed Trajectories
Farnaz Behnia, Dominik Karbowski, Vadim Sokolov
Generating realistic vehicle speed trajectories is a crucial component in evaluating vehicle fuel economy and in predictive control of self-driving cars. Traditional generative mod…
Merging Two Cultures: Deep and Statistical Learning
Anindya Bhadra, Jyotishka Datta, Nick Polson +2
Merging the two cultures of deep and statistical learning provides insights into structured high-dimensional data. Traditional statistical modeling is still a dominant strategy for…
Deep Learning Partial Least Squares
Nicholas Polson, Vadim Sokolov, Jianeng Xu
High dimensional data reduction techniques are provided by using partial least squares within deep learning. Our framework provides a nonlinear extension of PLS together with a dis…
Analyzing Stochastic Computer Models: A Review with Opportunities
Evan Baker, Pierre Barbillon, Arindam Fadikar +10
In modern science, computer models are often used to understand complex phenomena, and a thriving statistical community has grown around analyzing them. This review aims to bring a…
Eco-Mobility-on-Demand Fleet Control with Ride-Sharing
Xianan Huang, Boqi Li, Huei Peng +2
Shared Mobility-on-Demand using automated vehicles can reduce energy consumption and cost for future mobility. However, its full potential in energy saving has not been fully explo…
Solving Large-Scale 0-1 Knapsack Problems and its Application to Point Cloud Resampling
Duanshun Li, Jing Liu, Noseong Park +7
0-1 knapsack is of fundamental importance in computer science, business, operations research, etc. In this paper, we present a deep learning technique-based method to solve large-s…
Strategic Bayesian Asset Allocation
Vadim Sokolov, Michael Polson
Strategic asset allocation requires an investor to select stocks from a given basket of assets. The perspective of our investor is to maximize risk-adjusted alpha returns relative…
Data Augmentation for Bayesian Deep Learning
Yuexi Wang, Nicholas G. Polson, Vadim O. Sokolov
Deep Learning (DL) methods have emerged as one of the most powerful tools for functional approximation and prediction. While the representation properties of DL have been well stud…
Bayesian Regularization: From Tikhonov to Horseshoe
Nicholas G. Polson, Vadim Sokolov
Bayesian regularization is a central tool in modern-day statistical and machine learning methods. Many applications involve high-dimensional sparse signal recovery problems. The go…
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 Learning
Nicholas G. Polson, Vadim O. Sokolov
Deep learning (DL) is a high dimensional data reduction technique for constructing high-dimensional predictors in input-output models. DL is a form of machine learning that uses hi…
Deep Learning: Computational Aspects
Nicholas Polson, Vadim Sokolov
In this article we review computational aspects of Deep Learning (DL). Deep learning uses network architectures consisting of hierarchical layers of latent variables to construct p…
Deep Learning for Energy Markets
Michael Polson, Vadim Sokolov
Deep Learning is applied to energy markets to predict extreme loads observed in energy grids. Forecasting energy loads and prices is challenging due to sharp peaks and troughs that…
Effects of Predictive Real-Time Traffic Signal Information
Vadim Sokolov, David W. Etherington, Christian Schmid +3
This paper analyzes the impact of providing car drivers with predictive information on traffic signal timing in real-time, including time-to-green and green-wave speed recommendati…
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…