5 citations · 5 across the 5 of their papers we have counts for
12 papers
Computing Rule-Based Explanations by Leveraging Counterfactuals
Zixuan Geng, Maximilian Schleich, Dan Suciu
Sophisticated machine models are increasingly used for high-stakes decisions in everyday life. There is an urgent need to develop effective explanation techniques for such automate…
Optimizing Tensor Programs on Flexible Storage
Maximilian Schleich, Amir Shaikhha, Dan Suciu
Tensor programs often need to process large tensors (vectors, matrices, or higher order tensors) that require a specialized storage format for their memory layout. Several such lay…
GeCo: Quality Counterfactual Explanations in Real Time
Maximilian Schleich, Zixuan Geng, Yihong Zhang +1
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 deci…
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…