1 code implementation • 5 Jan 2021 • Maximilian Schleich, Zixuan Geng, Yihong Zhang, Dan Suciu
Machine learning is increasingly applied in high-stakes decision making that directly affect people's lives, and this leads to an increased demand for systems to explain their decisions.
no code implementations • 22 Dec 2018 • Mahmoud Abo Khamis, Ryan R. Curtin, Benjamin Moseley, Hung Q. Ngo, XuanLong Nguyen, Dan Olteanu, Maximilian Schleich
This new width is sandwiched between the submodular and the fractional hypertree widths.
no code implementations • 11 Oct 2019 • Ryan Curtin, Ben Moseley, Hung Q. Ngo, XuanLong Nguyen, Dan Olteanu, Maximilian Schleich
When the data matrix needs to be obtained from a relational database via a feature extraction query, the computation cost can be prohibitive, as the data matrix may be (much) larger than the total input relation size.
no code implementations • 10 Jan 2020 • Amir Shaikhha, Maximilian Schleich, Alexandru Ghita, Dan Olteanu
We consider the problem of training machine learning models over multi-relational data.
no code implementations • 15 Mar 2020 • Leopoldo Bertossi, Jordan Li, Maximilian Schleich, Dan Suciu, Zografoula Vagena
We propose a simple definition of an explanation for the outcome of a classifier based on concepts from causality.
no code implementations • 18 Sep 2020 • Guy Van den Broeck, Anton Lykov, Maximilian Schleich, Dan Suciu
First, we consider fully-factorized data distributions, and show that the complexity of computing the SHAP explanation is the same as the complexity of computing the expected value of the model.
no code implementations • 31 Oct 2022 • Zixuan Geng, Maximilian Schleich, Dan Suciu
We prove a Duality Theorem, showing that rule-based and counterfactual-based explanations are dual to each other, then use this observation to develop an efficient algorithm for computing rule-based explanations, which uses the counterfactual-based explanation as an oracle.