208 citations · 302 across the 11 of their papers we have counts for
4 papers · 1 filter
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…