Author

Mustafa Yalçiner

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Counterfactual Explanations for Incomplete Inputs Using Flow-Based Generative Models

This work proposes a framework for computing CXs for inputs with missing values using flow-based generative models and Mixed-Integer Linear Programming (MILP) and demonstrates that this method is able to generate CXs with higher validity, and lower cost than state-of-the-art methods relying on imputation.

Francesco Leofante, D. Neider, Mustafa Yalçiner · 0 citations

Conference Open access Sep 2026

eNNcode: Optimization-Based Analysis of Neural Networks

eNNcode is introduced, a user-friendly PyPI library that converts any piecewise-linear neural network in Open Neural Network Exchange (ONNX) format into a MILO instance, and supports arbitrary constraints on input and output nodes, as well as user-defined optimization objectives.

Muhammad Sabri Farag Atallah, Lukas Dankwart, D. Neider et al. · Proceedings of the Thirty-Fi... · 0 citations