From the 1 of 8 linked papers with an AI index.
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Generating Input Distributions for Explaining Portfolio Optimization Pipelines
Batuhan AtaÅ, NurÅen Aydın, E. Mehmet Kıral +1
We propose a predict-optimize-explain framework that uses gradient-based sample generation to interpret various portfolio models by identifying macroeconomic conditions that induce…
Explainable Optimization: A Call for Interdisciplinary Action
NurÅen Aydın, Å. Ä°lker Birbil, Ä°lker Küçükparlak +1
Operations research and management science models support decisions that affect patients, workers, citizens, and public institutions. Decision-makers, such as clinicians approving…
Counterfactual Explanations for Integer Optimization Problems
Felix Engelhardt, Jannis Kurtz, Å. Ä°lker Birbil +1
Counterfactual explanations (CEs) offer a human-understandable way to explain decisions by identifying specific changes to the input parameters of a base or present model that woul…
Machine Learning for K-adaptability in Two-stage Robust Optimization
Esther Julien, Krzysztof Postek, Å. Ä°lker Birbil
Two-stage robust optimization problems constitute one of the hardest optimization problem classes. One of the solution approaches to this class of problems is K-adaptability. This…
Counterfactual Explanations for Linear Optimization
Jannis Kurtz, Å. Ä°lker Birbil, Dick den Hertog
The concept of counterfactual explanations (CE) has emerged as one of the important concepts to understand the inner workings of complex AI systems. In this paper, we translate the…