74 citations · 101 across the 7 of their papers we have counts for
10 papers
Inverse Optimization for Routing Problems
Pedro Zattoni Scroccaro, Piet van Beek, Peyman Mohajerin Esfahani +1
We propose a method for learning decision-makers' behavior in routing problems using Inverse Optimization (IO). The IO framework falls into the supervised learning category and bui…
Scalable Bayesian Network Structure Learning with Splines
Charupriya Sharma, Peter van Beek
The graph structure of a Bayesian network (BN) can be learned from data using the well-known score-and-search approach. Previous work has shown that incorporating structured repres…
A Score-and-Search Approach to Learning Bayesian Networks with Noisy-OR Relations
Charupriya Sharma, Zhenyu A. Liao, James Cussens +1
A Bayesian network is a probabilistic graphical model that consists of a directed acyclic graph (DAG), where each node is a random variable and attached to each node is a condition…
Learning All Credible Bayesian Network Structures for Model Averaging
Zhenyu A. Liao, Charupriya Sharma, James Cussens +1
A Bayesian network is a widely used probabilistic graphical model with applications in knowledge discovery and prediction. Learning a Bayesian network (BN) from data can be cast as…
Finding All Bayesian Network Structures within a Factor of Optimal
Zhenyu A. Liao, Charupriya Sharma, James Cussens +1
A Bayesian network is a widely used probabilistic graphical model with applications in knowledge discovery and prediction. Learning a Bayesian network (BN) from data can be cast as…
Improved Image Selection for Stack-Based HDR Imaging
Peter van Beek
Stack-based high dynamic range (HDR) imaging is a technique for achieving a larger dynamic range in an image by combining several low dynamic range images acquired at different exp…