124 citations · 132 across the 6 of their papers we have counts for
16 papers
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…
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…