Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This datadriven model involves demand-driven aggregation of information sources, min...

Graphics Processing Units (GPUs) placed at our disposal an unprecedented computational-power, largely surpassing the performance of cutting-edge CPUs (Central Processing Units). The high-parallelism inherent to the GPU makes this device especially well-suited to address Machine Learning (ML) problems with prohibitively computational intensive tasks. Nevertheless, few ML algorithms have been imp...

Recently, Knowledge Discovery on Databases (KDD) has emerged as a promising research area encompassing methods from several disciplines. Particularly, the data mining step of KDD shares most of its goals with unsupervised learning. But data mining methods are biased towards statistical techniques arguing that Machine Learning (ML) methods are not suitable to deal with real-world databases. We c...

The Higgs Machine Learning Challenge was an open data analysis competition that took place between May and September 2014. Samples of simulated data from the ATLAS Experiment at the LHC corresponding to signal events with Higgs bosons decaying to τ+τ− together with background events were made available to the public through the website of the data science organization Kaggle (kaggle.com). Parti...

BACKGROUND Prior studies have demonstrated that cardiorespiratory fitness (CRF) is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined categories. The aim of this study is to present an evaluation and comparison of how machine learning techniques can be applied on medic...

Major cloud operators offer machine learning (ML) as a service, enabling customers who have the data but not ML expertise or infrastructure to train predictive models on this data. Existing ML-as-a-service platforms require users to reveal all training data to the service operator. We design, implement, and evaluate Chiron, a system for privacy-preserving machine learning as a service. First, C...

Emerging Machine Learning (ML) techniques, such as Deep Neural Network, are widely used in today’s applications and services. However, with social awareness of privacy and personal data rapidly rising, it becomes a pressing and challenging societal issue to both keep personal data private and benefit from the data analytics power of ML techniques at the same time. In this paper, we argue that t...

Stochastic Gradient Descent (SGD) is the standard numerical method used to solve the core optimization problem for the vast majority of machine learning (ML) algorithms. In the context of large scale learning, as utilized by many Big Data applications, efficient parallelization of SGD is in the focus of active research. Recently, we were able to show that the asynchronous communication paradigm...

Usually, performance is the primary objective in systems that make use of user modeling (Um) techniques. But since machine learning (Ml) in user modeling addresses several issues in the context of human computer interaction (Hci), the requirements on \performance" are manifold. Thus, machine learning for user model-ing (Ml4Um) has to meet several demands in order to satisfy the aims of involved...