Developing causal models from observational longitudinal studies is an important, ubiquitous problem in many disciplines. A disadvantage of current causal discover algorithms, howe...
Ridho Rahmadi, Perry Groot, Marianne Heins, Hans K...
The pervasiveness and availability of mobile phone data offer the opportunity of discovering usable knowledge about crowd behavior in urban environments. Cities can leverage such ...
Averaging a set of time series is a major topic for many temporal data mining tasks as summarization, extracting prototype or clustering. Time series averaging should deal with the...
A hybrid architecture is presented capable of online learning from both labeled and unlabeled samples. It combines both generative and discriminative objectives to derive a new var...
Alexander G. Ororbia II, David Reitter, Jian Wu, C...
This paper describes the winning solution to the Taxi Trip Time Prediction Challenge run by Kaggle.com. The goal of the competition was to build a predictive framework that is able...
A method to monitor communicable diseases based on health records is proposed. The method is applied to health facility records of malaria incidence in Uganda. This disease represe...
Ricardo Andrade Pacheco, Martin Gordon Mubangizi, ...
Abstract. The optimization of hyperparameters is often done manually or exhaustively but recent work has shown that automatic methods can optimize hyperparameters faster and even a...
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Th...
Abstract. This paper aims to design an algorithm dedicated to operational planning for microgrids in the challenging case where the scenarios of production and consumption are not ...
Experiments in high-energy astroparticle physics produce large amounts of data as continuous high-volume streams. Gaining insights from the observed data poses a number of challeng...
Abstract. Mining evolving datastreams raises the question how to extrapolate trends in the evolution of densities over time. While approaches for change diagnosis work well for int...