Maximilian Schleich

Orcid: 0009-0004-5502-869X

Affiliations:
  • University of Washington, WA, USA


According to our database1, Maximilian Schleich authored at least 22 papers between 2016 and 2024.

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Bibliography

2024
Optimizing Nested Recursive Queries.
Proc. ACM Manag. Data, February, 2024

2023
Optimizing Tensor Programs on Flexible Storage.
Proc. ACM Manag. Data, 2023

2022
Computing Rule-Based Explanations by Leveraging Counterfactuals.
Proc. VLDB Endow., 2022

On the Tractability of SHAP Explanations.
J. Artif. Intell. Res., 2022

2021
An Intermediate Representation for Hybrid Database and Machine Learning Workloads.
Proc. VLDB Endow., 2021

GeCo: Quality Counterfactual Explanations in Real Time.
Proc. VLDB Endow., 2021

Structure-Aware Machine Learning over Multi-Relational Databases.
Proceedings of the SIGMOD '21: International Conference on Management of Data, 2021

2020
Structure-aware machine learning over multi-relational databases.
PhD thesis, 2020

Learning Models over Relational Data Using Sparse Tensors and Functional Dependencies.
ACM Trans. Database Syst., 2020

Functional Aggregate Queries with Additive Inequalities.
ACM Trans. Database Syst., 2020

LMFAO: An Engine for Batches of Group-By Aggregates.
Proc. VLDB Endow., 2020

Causality-based Explanation of Classification Outcomes.
Proceedings of the Fourth Workshop on Data Management for End-To-End Machine Learning, 2020

Multi-layer optimizations for end-to-end data analytics.
Proceedings of the CGO '20: 18th ACM/IEEE International Symposium on Code Generation and Optimization, 2020

Rk-means: Fast Clustering for Relational Data.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Learning Models over Relational Data: A Brief Tutorial.
Proceedings of the Scalable Uncertainty Management - 13th International Conference, 2019

A Layered Aggregate Engine for Analytics Workloads.
Proceedings of the 2019 International Conference on Management of Data, 2019

2018
AC/DC: In-Database Learning Thunderstruck.
Proceedings of the Second Workshop on Data Management for End-To-End Machine Learning, 2018

In-Database Learning with Sparse Tensors.
Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, 2018

2017
In-Database Factorized Learning.
Proceedings of the 11th Alberto Mendelzon International Workshop on Foundations of Data Management and the Web, 2017

2016
Factorized Databases.
SIGMOD Rec., 2016

F: Regression Models over Factorized Views.
Proc. VLDB Endow., 2016

Learning Linear Regression Models over Factorized Joins.
Proceedings of the 2016 International Conference on Management of Data, 2016


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