13 citations · 31 across the 8 of their papers we have counts for
13 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…
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…
Learning Models over Relational Data: A Brief Tutorial
Maximilian Schleich, Dan Olteanu, Mahmoud Abo-Khamis +2
This tutorial overviews the state of the art in learning models over relational databases and makes the case for a first-principles approach that exploits recent developments in da…
Rk-means: Fast Clustering for Relational Data
Ryan Curtin, Ben Moseley, Hung Q. Ngo +3
Conventional machine learning algorithms cannot be applied until a data matrix is available to process. When the data matrix needs to be obtained from a relational database via a f…
A Layered Aggregate Engine for Analytics Workloads
Maximilian Schleich, Dan Olteanu, Mahmoud Abo Khamis +2
This paper introduces LMFAO (Layered Multiple Functional Aggregate Optimization), an in-memory optimization and execution engine for batches of aggregates over the input database.…
Functional Aggregate Queries with Additive Inequalities
Mahmoud Abo Khamis, Ryan R. Curtin, Benjamin Moseley +4
Motivated by fundamental applications in databases and relational machine learning, we formulate and study the problem of answering functional aggregate queries (FAQ) in which some…
AC/DC: In-Database Learning Thunderstruck
Mahmoud Abo Khamis, Hung Q. Ngo, XuanLong Nguyen +2
We report on the design and implementation of the AC/DC gradient descent solver for a class of optimization problems over normalized databases. AC/DC decomposes an optimization pro…
Learning Models over Relational Data using Sparse Tensors and Functional Dependencies
Mahmoud Abo Khamis, Hung Q. Ngo, XuanLong Nguyen +2
Integrated solutions for analytics over relational databases are of great practical importance as they avoid the costly repeated loop data scientists have to deal with on a daily b…