no code implementations • 20 Apr 2022 • Małgorzata Łazuka, Thomas Parnell, Andreea Anghel, Haralampos Pozidis
Our experiments indicate that (a) many state-of-the-art cloud configuration solutions can be adapted to multi-cloud, with best results obtained for adaptations which utilize the hierarchical structure of the multi-cloud configuration domain, (b) hierarchical methods from AutoML can be used for the multi-cloud configuration task and can outperform state-of-the-art cloud configuration solutions and (c) CB achieves competitive or lower regret relative to other tested algorithms, whilst also identifying configurations that have 65% lower median cost and 20% lower median time in production, compared to choosing a random provider and configuration.
no code implementations • 29 Sep 2021 • Malgorzata Lazuka, Thomas Parnell, Andreea Anghel, Haralampos Pozidis
Multi-cloud computing has become increasingly popular with enterprises looking to avoid vendor lock-in.
2 code implementations • NeurIPS 2020 • Thomas Parnell, Andreea Anghel, Malgorzata Lazuka, Nikolas Ioannou, Sebastian Kurella, Peshal Agarwal, Nikolaos Papandreou, Haralampos Pozidis
At each boosting iteration, their goal is to find the base hypothesis, selected from some base hypothesis class, that is closest to the Newton descent direction in a Euclidean sense.
no code implementations • 22 Mar 2019 • Alessandro De Palma, Celestine Mendler-Dünner, Thomas Parnell, Andreea Anghel, Haralampos Pozidis
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 stochastic process.
no code implementations • 12 Sep 2018 • Andreea Anghel, Nikolaos Papandreou, Thomas Parnell, Alessandro De Palma, Haralampos Pozidis
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 learning tasks.
no code implementations • NeurIPS 2018 • Celestine Dünner, Thomas Parnell, Dimitrios Sarigiannis, Nikolas Ioannou, Andreea Anghel, Gummadi Ravi, Madhusudanan Kandasamy, Haralampos Pozidis
We describe a new software framework for fast training of generalized linear models.
no code implementations • 5 Dec 2016 • Celestine Dünner, Thomas Parnell, Kubilay Atasu, Manolis Sifalakis, Haralampos Pozidis
We begin by analyzing the characteristics of a state-of-the-art distributed machine learning algorithm implemented in Spark and compare it to an equivalent reference implementation using the high performance computing framework MPI.