208 citations · 302 across the 11 of their papers we have counts for
12 papers
A Constraint Programming Approach for n-Day Lookahead Playoff Clinching in the NHL
Gili Rosenberg, Kyle E. C. Booth, J. Kyle Brubaker +1
In professional sports, a team has clinched the playoffs if they are guaranteed a postseason spot, regardless of the outcomes of any remaining games. As the season progresses, spor…
Crystal structure prediction using graph neural combinatorial optimization
Stavros Gerolymatos, J. Kyle Brubaker, Martin J. A. Schuetz +1
Crystalline materials are widely used in technological applications, yet their discovery remains a significant challenge. As their properties are driven by structure, crystal struc…
Optimization of Next-Day Delivery Coverage using Constraint Programming and Random Key Optimizers
Kyle Brubaker, Kyle E. C. Booth, Martin J. A. Schuetz +4
We consider the logistics network of an e-commerce retailer, specifically the so-called "middle mile" network, that routes inventory from supply warehouses to distribution stations…
Quadratic unconstrained binary optimization and constraint programming approaches for lattice-based cyclic peptide docking
J. Kyle Brubaker, Kyle E. C. Booth, Akihiko Arakawa +4
The peptide-protein docking problem is an important problem in structural biology that facilitates rational and efficient drug design. In this work, we explore modeling and solving…
Scalable iterative pruning of large language and vision models using block coordinate descent
Gili Rosenberg, J. Kyle Brubaker, Martin J. A. Schuetz +4
Pruning neural networks, which involves removing a fraction of their weights, can often maintain high accuracy while significantly reducing model complexity, at least up to a certa…
Explainable AI using expressive Boolean formulas
Gili Rosenberg, J. Kyle Brubaker, Martin J. A. Schuetz +6
We propose and implement an interpretable machine learning classification model for Explainable AI (XAI) based on expressive Boolean formulas. Potential applications include credit…
Designing Quantum Annealing Schedules using Bayesian Optimization
Jernej Rudi Finžgar, Martin J. A. Schuetz, J. Kyle Brubaker +2
We propose and analyze the use of Bayesian optimization techniques to design quantum annealing schedules with minimal user and resource requirements. We showcase our scheme with re…
Reply to: Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems
Martin J. A. Schuetz, J. Kyle Brubaker, Helmut G. Katzgraber
We provide a comprehensive reply to the comment written by Stefan Boettcher [arXiv:2210.00623] and argue that the comment singles out one particular non-representative example prob…
Reply to: Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set
Martin J. A. Schuetz, J. Kyle Brubaker, Helmut G. Katzgraber
We provide a comprehensive reply to the comment written by Chiara Angelini and Federico Ricci-Tersenghi [arXiv:2206.13211] and argue that the comment singles out one particular non…
Optimization of Robot Trajectory Planning with Nature-Inspired and Hybrid Quantum Algorithms
Martin J. A. Schuetz, J. Kyle Brubaker, Henry Montagu +6
We solve robot trajectory planning problems at industry-relevant scales. Our end-to-end solution integrates highly versatile random-key algorithms with model stacking and ensemble…
Graph Coloring with Physics-Inspired Graph Neural Networks
Martin J. A. Schuetz, J. Kyle Brubaker, Zhihuai Zhu +1
We show how graph neural networks can be used to solve the canonical graph coloring problem. We frame graph coloring as a multi-class node classification problem and utilize an uns…
Combinatorial Optimization with Physics-Inspired Graph Neural Networks
Martin J. A. Schuetz, J. Kyle Brubaker, Helmut G. Katzgraber
Combinatorial optimization problems are pervasive across science and industry. Modern deep learning tools are poised to solve these problems at unprecedented scales, but a unifying…