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Article
Open AccessThe spatiotemporal profile and adaptation determine the joint effects and interactions of multiple stressors
Biodiversity is declining worldwide as ecosystems are increasingly threatened by multiple stressors associated with anthropogenic global change. Stressors frequently co-occur across scales spatially and tempor...
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Chapter and Conference Paper
Ordinal Regression for Difficulty Prediction of StepMania Levels
StepMania is a popular open-source clone of a rhythm-based video game. As is common in popular games, there is a large number of community-designed levels. It is often difficult for players and level authors t...
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Chapter and Conference Paper
Image Anomaly Detection with Generative Adversarial Networks
Many anomaly detection methods exist that perform well on low-dimensional problems however there is a notable lack of effective methods for high-dimensional spaces, such as images. Inspired by recent successes...
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Article
Sparse probit linear mixed model
Linear mixed models (LMMs) are important tools in statistical genetics. When used for feature selection, they allow to find a sparse set of genetic traits that best predict a continuous phenotype of interest, ...
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Chapter and Conference Paper
Bayesian Nonlinear Support Vector Machines for Big Data
We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster th...
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Article
Open AccessCombining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies
The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype u...
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Chapter and Conference Paper
Huber-Norm Regularization for Linear Prediction Models
In order to avoid overfitting, it is common practice to regularize linear prediction models using squared or absolute-value norms of the model parameters. In our article we consider a new method of regularizat...
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Article
Probabilistic clustering of time-evolving distance data
We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points. The proposed method utilizes the information given by adjacent time...
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Chapter and Conference Paper
Opening the Black Box: Revealing Interpretable Sequence Motifs in Kernel-Based Learning Algorithms
This work is in the context of kernel-based learning algorithms for sequence data. We present a probabilistic approach to automatically extract, from the output of such string-kernel-based learning algorithms,...
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Article
Regularization-Based Multitask Learning With Applications to Genome Biology and Biological Imaging
The aim of multitask learning is to improve the generalization performance of a set of related tasks by exploiting complementary information about the tasks. In this paper, we review established approaches for...
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Chapter
Multi-task Learning for Computational Biology: Overview and Outlook
We present an overview of the field of regularization-based multi-task learning, which is a relatively recent offshoot of statistical machine learning. We discuss the foundations as well as some of the recent ...
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Chapter and Conference Paper
Efficient Training of Graph-Regularized Multitask SVMs
We present an optimization framework for graph-regularized multi-task SVMs based on the primal formulation of the problem. Previous approaches employ a so-called multi-task kernel (MTK) and thus are inapplicable ...
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Chapter and Conference Paper
Transfer Learning with Adaptive Regularizers
The success of regularized risk minimization approaches to classification with linear models depends crucially on the selection of a regularization term that matches with the learning task at hand. If the nece...
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Chapter and Conference Paper
A Unifying View of Multiple Kernel Learning
Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different formulations of objectives and v...
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Chapter and Conference Paper
Active and Semi-supervised Data Domain Description
Data domain description techniques aim at deriving concise descriptions of objects belonging to a category of interest. For instance, the support vector domain description (SVDD) learns a hypersphere enclosing...
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Chapter and Conference Paper
Feature Selection for Density Level-Sets
A frequent problem in density level-set estimation is the choice of the right features that give rise to compact and concise representations of the observed data. We present an efficient feature selection meth...
