From the 1 of 8 linked papers with an AI index.
8 papers
Learning an Interpretable Risk Scoring System for Maximizing Decision Net Benefit
Wenhao Chi, Å. Ä°lker Birbil
The paper introduces an interpretable risk scoring system that directly maximizes decision net benefit by formulating the problem as a sparse integer linear program, and shows it m…
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…
Output-Constrained Decision Trees
Hüseyin Tunç, DoÄanay Ãzese, Å. Ä°lker Birbil +3
Incorporating domain-specific constraints into machine learning models is essential for generating predictions that are both accurate and feasible in real-world applications. This…
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…
Generating Samples to Probe Trained Models
Eren Mehmet Kıral, NurÅen Aydın, Å. Ä°lker Birbil
There is a growing need for investigating how machine learning models operate. With this work, we aim to understand trained machine learning models by questioning their data prefer…