5 citations · 5 across the 5 of their papers we have counts for
4 papers · 1 filter
On the Tractability of SHAP Explanations
Guy Van den Broeck, Anton Lykov, Maximilian Schleich +1
SHAP explanations are a popular feature-attribution mechanism for explainable AI. They use game-theoretic notions to measure the influence of individual features on the prediction…
LMFAO: An Engine for Batches of Group-By Aggregates
Maximilian Schleich, Dan Olteanu
LMFAO is an in-memory optimization and execution engine for large batches of group-by aggregates over joins. Such database workloads capture the data-intensive computation of a var…
Causality-based Explanation of Classification Outcomes
Leopoldo Bertossi, Jordan Li, Maximilian Schleich +2
We propose a simple definition of an explanation for the outcome of a classifier based on concepts from causality. We compare it with previously proposed notions of explanation, an…
Multi-layer Optimizations for End-to-End Data Analytics
Amir Shaikhha, Maximilian Schleich, Alexandru Ghita +1
We consider the problem of training machine learning models over multi-relational data. The mainstream approach is to first construct the training dataset using a feature extractio…