Benjamin Hilprecht
Benjamin Hilprecht
Research Assistant, Data Management Lab, Computer Science
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DeepDB: learn from data, not from queries!
B Hilprecht, A Schmidt, M Kulessa, A Molina, K Kersting, C Binnig
PVLDB 13 (7), 992--1005, 2020
Monte Carlo and Reconstruction Membership Inference Attacks against Generative Models.
B Hilprecht, M Härterich, D Bernau
Proc. Priv. Enhancing Technol. 2019 (4), 232-249, 2019
Learning a partitioning advisor for cloud databases
B Hilprecht, C Binnig, U Röhm
Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020
Towards learning a partitioning advisor with deep reinforcement learning
B Hilprecht, C Binnig, U Röhm
Proceedings of the Second International Workshop on Exploiting Artificial …, 2019
Model-based approximate query processing
M Kulessa, A Molina, C Binnig, B Hilprecht, K Kersting
arXiv preprint arXiv:1811.06224, 2018
DBMS Fitting: Why should we learn what we already know?
B Hilprecht, C Binnig, T Bang, M El-Hindi, B Hättasch, A Khanna, ...
CIDR, 2020
Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction
B Hilprecht, C Binnig
arXiv preprint arXiv:2201.00561, 2022
ReStore-Neural Data Completion for Relational Databases
B Hilprecht, C Binnig
Proceedings of the 2021 International Conference on Management of Data, 710-722, 2021
One Model to Rule them All: Towards Zero-Shot Learning for Databases
B Hilprecht, C Binnig
arXiv preprint arXiv:2105.00642, 2021
Accurately identifying members of training data in variational autoencoders by reconstruction error
B Hilprecht, D Bernau, M Haerterich
US Patent App. 16/219,645, 2020
Computer systems for detecting training data usage in generative models
M Haerterich, B Hilprecht, D Bernau
US Patent App. 16/140,022, 2020
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