7 papers
BREAD: Baseline-Referenced Explanations for Anomaly Diagnosis
Jiaqi Qiu, Rob Goedhart, Jannis Kurtz +1
Artificial Intelligence (AI)-based prospective anomaly detection methods are increasingly deployed in high-dimensional and nonlinear settings. Among these approaches, AI-based stat…
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…
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…
A K-adaptability Approach to Proton Radiation Therapy Robust Treatment Planning
Zihang Qiu, Ali Ajdari, Mislav BobiÄ +4
Uncertainties such as setup and range errors can significantly compromise proton therapy. A discrete uncertainty set is often constructed to represent different uncertainty scenari…
Coherent Local Explanations for Mathematical Optimization
Daan Otto, Jannis Kurtz, S. Ilker Birbil
The surge of explainable artificial intelligence methods seeks to enhance transparency and explainability in machine learning models. At the same time, there is a growing demand fo…
Neur2BiLO: Neural Bilevel Optimization
Justin Dumouchelle, Esther Julien, Jannis Kurtz +1
Bilevel optimization deals with nested problems in which a leader takes the first decision to minimize their objective function while accounting for a follower's best-response reac…