19 citations · 72 across the 23 of their papers we have counts for
10 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.…
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…
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…
Finding Regions of Counterfactual Explanations via Robust Optimization
Donato Maragno, Jannis Kurtz, Tabea E. Röber +3
Counterfactual explanations play an important role in detecting bias and improving the explainability of data-driven classification models. A counterfactual explanation (CE) is a m…
Counterfactual Explanations Using Optimization With Constraint Learning
Donato Maragno, Tabea E. Röber, Ilker Birbil
To increase the adoption of counterfactual explanations in practice, several criteria that these should adhere to have been put forward in the literature. We propose counterfactual…
Learning with Subset Stacking
Ş. İlker Birbil, Sinan Yıldırım, Samet Çopur +1
We propose a new regression algorithm that learns from a set of input-output pairs. Our algorithm is designed for populations where the relation between the input variables and the…
Bolstering Stochastic Gradient Descent with Model Building
S. Ilker Birbil, Ozgur Martin, Gonenc Onay +1
Stochastic gradient descent method and its variants constitute the core optimization algorithms that achieve good convergence rates for solving machine learning problems. These rat…
Rule Generation for Classification: Scalability, Interpretability, and Fairness
Tabea E. Röber, Adia C. Lumadjeng, M. Hakan Akyüz +1
We introduce a new rule-based optimization method for classification with constraints. The proposed method leverages column generation for linear programming, and hence, is scalabl…
Differentially Private Accelerated Optimization Algorithms
Nurdan Kuru, Ş. İlker Birbil, Mert Gurbuzbalaban +1
We present two classes of differentially private optimization algorithms derived from the well-known accelerated first-order methods. The first algorithm is inspired by Polyak's he…