45 citations · 63 across the 3 of their papers we have counts for
4 papers
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…
Linear-Complexity Relaxed Word Mover's Distance with GPU Acceleration
Kubilay Atasu, Thomas Parnell, Celestine Dünner +6
The amount of unstructured text-based data is growing every day. Querying, clustering, and classifying this big data requires similarity computations across large sets of documents…
Understanding and Optimizing the Performance of Distributed Machine Learning Applications on Apache Spark
Celestine Dünner, Thomas Parnell, Kubilay Atasu +2
In this paper we explore the performance limits of Apache Spark for machine learning applications. We begin by analyzing the characteristics of a state-of-the-art distributed machi…