Modeling the dependence between outputs is a fundamental challenge in multilabel classification. In this work we show that a generic regularized nonlinearity mapping independent predictions to joint predictions is sufficient to achieve state-of-the-art performance on a variety of benchmark problems. Crucially, we compute the joint predictions without ever obtaining any independent predictions, ...

Ensemble Kalman Filtering is a sequential Monte Carlo method commonly used in meteorology to track atmospheric states and make numerical weather predictions (NWP). With the intent to introduce statisticians to this important area of application we address some of the practical aspects of the ensemble Kalman Filter in dynamic systems. We focus on three topics related to NWP: extending the ensemb...

Except for the excellence of their material and their focus on criminal behaviour there is little common ground between these two books. The former describes an original research into the prediction of success and failure among Borstal boys, carried out jointly by a criminologist and a statistician of distinction in their respective fields. The latter consists of a series of lectures given unde...

This paper presents methods to predict retrieval terms from relevant/surrounding words or descriptive texts in Japanese by using deep learning methods, which are implemented with stacked denoising autoencoders (SdA), as well as deep belief networks (DBN). To determine the effectiveness of using DBN and SdA for this task, we compare them with conventional machine learning methods, i.e., multi-la...

Protein-protein interactions (PPI) occur at almost every level of cell functions. The identification of interactions among proteins provides a global picture of cellular functions and biological processes. It is also an essential step in the construction of PPI networks for human and other organisms. PPI prediction has been considered to be a promising alternative to the traditional drug design...

The phasic firing rate of midbrain dopamine neurons has been shown to respond both to the receipt of rewarding stimuli, and the degree to which such stimuli are anticipated by the recipient. This has led to the hypothesis that these neurons encode reward prediction error (RPE)-the difference between how rewarding an event is, and how rewarding it was expected to be. However, the RPE model is on...

Online Social Networks are growing exponentially due to which a lot of researchers are working on Social Network analysis. Link Prediction is a task of predicting new links that may occur in future in the social network. The link prediction problem has generated a lot of interest due its widespread applicability across many domains. We conducted a study on the different methods that have been d...

Promising approaches to structured learning problems have recently been developed in the maximum margin framework. Unfortunately, algorithms that are computationally and memory efficient enough to solve large scale problems have lagged behind. We propose using simple subgradient-based techniques for optimizing a regularized risk formulation of these problems in both online and batch settings, a...