1 citations · 1 across the 4 of their papers we have counts for
5 papers
Generating Input Distributions for Explaining Portfolio Optimization Pipelines
Batuhan Ataş, Nurşen 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
Nurşen Aydın, Ş. İlker Birbil, İlker 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, Doğanay Özese, Ş. İlker 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…
Fixing confirmation bias in feature attribution methods via semantic match
Giovanni Cinà, Daniel Fernandez-Llaneza, Ludovico Deponte +6
Feature attribution methods have become a staple method to disentangle the complex behavior of black box models. Despite their success, some scholars have argued that such methods…
An Alternative Globalization Strategy for Unconstrained Optimization
Figen Öztoprak, Ş. İlker Birbil
We propose a new globalization strategy that can be used in unconstrained optimization algorithms to support rapid convergence from remote starting points. Our approach is based on…