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01/27/2021
Periodic seismicity detection without declustering
Any periodic variations of earthquake occurrence rates in response to sm...
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01/13/2021
Designing Machine Learning Toolboxes: Concepts, Principles and Patterns
Machine learning (ML) and AI toolboxes such as scikit-learn or Weka are ...
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08/18/2020
mlr3proba: An R Package for Machine Learning in Survival Analysis
As machine learning has become increasingly popular over the last few de...
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04/18/2020
Kernels for time series with irregularly-spaced multivariate observations
Time series are an interesting frontier for kernel-based methods, for th...
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09/17/2019
sktime: A Unified Interface for Machine Learning with Time Series
We present sktime -- a new scikit-learn compatible Python library with a...
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01/11/2019
Machine Learning Automation Toolbox (MLaut)
In this paper we present MLaut (Machine Learning AUtomation Toolbox) for...
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12/21/2018
Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness
Machine learning (ML), artificial intelligence (AI) and other modern sta...
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12/18/2018
NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature
Objective: To determine the completeness of argumentative steps necessar...
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07/04/2018
Modeling outcomes of soccer matches
We compare various extensions of the Bradley-Terry model and a hierarchi...
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01/02/2018
Probabilistic supervised learning
Predictive modelling and supervised learning are central to modern data ...
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11/16/2017
Predictive Independence Testing, Predictive Conditional Independence Testing, and Predictive Graphical Modelling
Testing (conditional) independence of multivariate random variables is a...
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01/27/2017
Modelling Competitive Sports: Bradley-Terry-Élő Models for Supervised and On-Line Learning of Paired Competition Outcomes
Prediction and modelling of competitive sports outcomes has received muc...
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01/29/2016
Kernels for sequentially ordered data
We present a novel framework for kernel learning with sequential data of...
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05/05/2015
Prediction and Quantification of Individual Athletic Performance
We provide scientific foundations for athletic performance prediction on...
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11/28/2014
Learning with Algebraic Invariances, and the Invariant Kernel Trick
When solving data analysis problems it is important to integrate prior k...
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03/04/2014
Matroid Regression
We propose an algebraic combinatorial method for solving large sparse li...
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02/17/2014
The Algebraic Approach to Phase Retrieval and Explicit Inversion at the Identifiability Threshold
We study phase retrieval from magnitude measurements of an unknown signa...
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02/01/2014
Dual-to-kernel learning with ideals
In this paper, we propose a theory which unifies kernel learning and sym...
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09/12/2013
Efficient Orthogonal Tensor Decomposition, with an Application to Latent Variable Model Learning
Decomposing tensors into orthogonal factors is a well-known task in stat...
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02/21/2013
Obtaining error-minimizing estimates and universal entry-wise error bounds for low-rank matrix completion
We propose a general framework for reconstructing and denoising single e...
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02/12/2013
Coherence and sufficient sampling densities for reconstruction in compressed sensing
We give a new, very general, formulation of the compressed sensing probl...
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11/30/2012
Approximate Rank-Detecting Factorization of Low-Rank Tensors
We present an algorithm, AROFAC2, which detects the (CP-)rank of a degre...
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11/17/2012
The Algebraic Combinatorial Approach for Low-Rank Matrix Completion
We present a novel algebraic combinatorial view on low-rank matrix compl...
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08/06/2011
