43 citations · 114 across the 8 of their papers we have counts for
10 papers · 1 filter
Harnessing Heterogeneity: Learning from Decomposed Feedback in Bayesian Modeling
Kai Wang, Bryan Wilder, Sze-chuan Suen +2
There is significant interest in learning and optimizing a complex system composed of multiple sub-components, where these components may be agents or autonomous sensors. Among the…
End-to-End Constrained Optimization Learning: A Survey
James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck +1
This paper surveys the recent attempts at leveraging machine learning to solve constrained optimization problems. It focuses on surveying the work on integrating combinatorial solv…
Automatically Learning Compact Quality-aware Surrogates for Optimization Problems
Kai Wang, Bryan Wilder, Andrew Perrault +1
Solving optimization problems with unknown parameters often requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using…
Fuzzy c-Means Clustering for Persistence Diagrams
Thomas Davies, Jack Aspinall, Bryan Wilder +1
Persistence diagrams concisely represent the topology of a point cloud whilst having strong theoretical guarantees, but the question of how to best integrate this information into…
MIPaaL: Mixed Integer Program as a Layer
Aaron Ferber, Bryan Wilder, Bistra Dilkina +1
Machine learning components commonly appear in larger decision-making pipelines; however, the model training process typically focuses only on a loss that measures accuracy between…
SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
Po-Wei Wang, Priya L. Donti, Bryan Wilder +1
Integrating logical reasoning within deep learning architectures has been a major goal of modern AI systems. In this paper, we propose a new direction toward this goal by introduci…