19 citations · 72 across the 23 of their papers we have counts for
23 papers
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…
Modeling Alzheimer's Disease: Bayesian Copula Graphical Model from Demographic, Cognitive, and Neuroimaging Data
Lucas Vogels, Reza Mohammadi, Marit Schoonhoven +2
The early detection of Alzheimer's disease (AD) requires an understanding of the relationships between a wide range of features. Conditional independencies and partial correlations…
Counterfactual Explanations for Linear Optimization
Jannis Kurtz, Ş. İlker 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…
From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifesto
Segev Wasserkrug, Leonard Boussioux, Dick den Hertog +4
Significantly simplifying the creation of optimization models for real-world business problems has long been a major goal in applying mathematical optimization more widely to impor…
Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison
Lucas Vogels, Reza Mohammadi, Marit Schoonhoven +1
Gaussian graphical models provide a powerful framework to reveal the conditional dependency structure between multivariate variables. The process of uncovering the conditional depe…
Scalable Bayesian Structure Learning for Gaussian Graphical Models Using Marginal Pseudo-likelihood
Reza Mohammadi, Marit Schoonhoven, Lucas Vogels +1
Bayesian methods for learning Gaussian graphical models offer a principled framework for quantifying model uncertainty and incorporating prior knowledge. However, their scalability…
Semantic match: Debugging feature attribution methods in XAI for healthcare
Giovanni Cinà, Tabea E. Röber, Rob Goedhart +1
The recent spike in certified Artificial Intelligence (AI) tools for healthcare has renewed the debate around adoption of this technology. One thread of such debate concerns Explai…
Differentially Private Distributed Bayesian Linear Regression with MCMC
Barış Alparslan, Sinan Yıldırım, Ş. İlker Birbil
We propose a novel Bayesian inference framework for distributed differentially private linear regression. We consider a distributed setting where multiple parties hold parts of the…
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…
Machine Learning for K-adaptability in Two-stage Robust Optimization
Esther Julien, Krzysztof Postek, Ş. İlker 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 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…
Why we do need Explainable AI for Healthcare
Giovanni Cinà, Tabea Röber, Rob Goedhart +1
The recent spike in certified Artificial Intelligence (AI) tools for healthcare has renewed the debate around adoption of this technology. One thread of such debate concerns Explai…
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…
Mixed-Integer Optimization with Constraint Learning
Donato Maragno, Holly Wiberg, Dimitris Bertsimas +3
We establish a broad methodological foundation for mixed-integer optimization with learned constraints. We propose an end-to-end pipeline for data-driven decision making in which c…
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…
Parallel Stochastic Gradient Markov Chain Monte Carlo for Matrix Factorisation Models
Umut Şimşekli, Hazal Koptagel, Hakan Güldaş +3
For large matrix factorisation problems, we develop a distributed Markov Chain Monte Carlo (MCMC) method based on stochastic gradient Langevin dynamics (SGLD) that we call Parallel…