3 citations · 6 across the 2 of their papers we have counts for
4 papers
DeepDB: Learn from Data, not from Queries!
Benjamin Hilprecht, Andreas Schmidt, Moritz Kulessa +3
The typical approach for learned DBMS components is to capture the behavior by running a representative set of queries and use the observations to train a machine learning model. T…
Reconstruction and Membership Inference Attacks against Generative Models
Benjamin Hilprecht, Martin Härterich, Daniel Bernau
We present two information leakage attacks that outperform previous work on membership inference against generative models. The first attack allows membership inference without ass…
Learning a Partitioning Advisor with Deep Reinforcement Learning
Benjamin Hilprecht, Carsten Binnig, Uwe Roehm
Commercial data analytics products such as Microsoft Azure SQL Data Warehouse or Amazon Redshift provide ready-to-use scale-out database solutions for OLAP-style workloads in the c…
Model-based Approximate Query Processing
Moritz Kulessa, Alejandro Molina, Carsten Binnig +2
Interactive visualizations are arguably the most important tool to explore, understand and convey facts about data. In the past years, the database community has been working on di…