19 citations · 72 across the 23 of their papers we have counts for
6 papers · 1 filter
Local Violation Certification for Linear Predict-Then-Optimize Pipelines
Ş. İlker Birbil, Wenhao Chi
Data-driven decision pipelines combining predictive machine learning models with downstream optimization software are increasingly used to make high-stakes operational decisions. C…
Optimization with Dynamic Constraint Learning (DCL)
Ezgi Oztekin, Figen Oztoprak, S. Ilker Birbil
We propose Dynamic Constraint Learning (DCL), a data-driven framework for constrained optimization when constraint functions are unknown and cannot be queried during optimization.…
Improving understanding and trust in AI: How users benefit from interval-based counterfactual explanations
Tabea E. Röber, Paul Festor, Rob Goedhart +2
Experimental user studies evaluating the effectiveness of different subtypes of post-hoc explanations for black-box models are largely nonexistent. Therefore, the aim of this study…
Learning an Interpretable Risk Scoring System for Maximizing Decision Net Benefit
Wenhao Chi, Ş. İlker Birbil
Risk scoring systems are widely used in high-stakes domains to assist decision-making. However, existing approaches often focus on optimizing predictive accuracy or likelihood-base…
Linear Model Extraction via Factual and Counterfactual Queries
Daan Otto, Jannis Kurtz, Dick den Hertog +1
In model extraction attacks, the goal is to reveal the parameters of a black-box machine learning model by querying the model for a selected set of data points. Due to an increasin…
ECSEL: Explainable Classification via Signomial Equation Learning
Adia Lumadjeng, Ilker Birbil, Erman Acar
We introduce ECSEL, an explainable classification method that learns formal expressions in the form of signomial equations, motivated by the observation that many symbolic regressi…