45 citations · 85 across the 8 of their papers we have counts for
6 papers · 1 filter
Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection
Jovan Blanuša, Maximo Cravero Baraja, Andreea Anghel +4
In this paper, we present "Graph Feature Preprocessor", a software library for detecting typical money laundering patterns in financial transaction graphs in real time. These patte…
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…
Breadth-first, Depth-next Training of Random Forests
Andreea Anghel, Nikolas Ioannou, Thomas Parnell +3
In this paper we analyze, evaluate, and improve the performance of training Random Forest (RF) models on modern CPU architectures. An exact, state-of-the-art binary decision tree b…
Sampling Acquisition Functions for Batch Bayesian Optimization
Alessandro De Palma, Celestine Mendler-Dünner, Thomas Parnell +2
We present Acquisition Thompson Sampling (ATS), a novel technique for batch Bayesian Optimization (BO) based on the idea of sampling multiple acquisition functions from a stochasti…
Benchmarking and Optimization of Gradient Boosting Decision Tree Algorithms
Andreea Anghel, Nikolaos Papandreou, Thomas Parnell +2
Gradient boosting decision trees (GBDTs) have seen widespread adoption in academia, industry and competitive data science due to their state-of-the-art performance in many machine…
Snap ML: A Hierarchical Framework for Machine Learning
Celestine Dünner, Thomas Parnell, Dimitrios Sarigiannis +5
We describe a new software framework for fast training of generalized linear models. The framework, named Snap Machine Learning (Snap ML), combines recent advances in machine learn…