898 citations · 1.2k across the 19 of their papers we have counts for
3 papers · 1 filter
Towards a General Framework for ML-based Self-tuning Databases
Thomas Schmied, Diego Didona, Andreas Döring +2
Machine learning (ML) methods have recently emerged as an effective way to perform automated parameter tuning of databases. State-of-the-art approaches include Bayesian optimizatio…
SnapBoost: A Heterogeneous Boosting Machine
Thomas Parnell, Andreea Anghel, Malgorzata Lazuka +5
Modern gradient boosting software frameworks, such as XGBoost and LightGBM, implement Newton descent in a functional space. At each boosting iteration, their goal is to find the ba…
Differentially Private Stochastic Coordinate Descent
Georgios Damaskinos, Celestine Mendler-Dünner, Rachid Guerraoui +2
In this paper we tackle the challenge of making the stochastic coordinate descent algorithm differentially private. Compared to the classical gradient descent algorithm where updat…