179 citations · 420 across the 27 of their papers we have counts for
32 papers
Designing Machine Learning Toolboxes: Concepts, Principles and Patterns
Franz J. Király, Markus Löning, Anthony Blaom +2
Machine learning (ML) and AI toolboxes such as scikit-learn or Weka are workhorses of contemporary data scientific practice -- their central role being enabled by usable yet powerf…
distr6: R6 Object-Oriented Probability Distributions Interface in R
Raphael Sonabend, Franz Kiraly
distr6 is an object-oriented (OO) probability distributions interface leveraging the extensibility and scalability of R6, and the speed and efficiency of Rcpp. Over 50 probability…
MLJ: A Julia package for composable machine learning
Anthony D. Blaom, Franz Kiraly, Thibaut Lienart +3
MLJ (Machine Learing in Julia) is an open source software package providing a common interface for interacting with machine learning models written in Julia and other languages. It…
Forecasting with sktime: Designing sktime's New Forecasting API and Applying It to Replicate and Extend the M4 Study
Markus Löning, Franz Király
We present a new open-source framework for forecasting in Python. Our framework forms part of sktime, a more general machine learning toolbox for time series with scikit-learn comp…
A tale of two toolkits, report the first: benchmarking time series classification algorithms for correctness and efficiency
Anthony Bagnall, Franz Király, Markus Löning +2
sktime is an open source, Python based, sklearn compatible toolkit for time series analysis developed by researchers at the University of East Anglia (UEA), University College Lond…
sktime: A Unified Interface for Machine Learning with Time Series
Markus Löning, Anthony Bagnall, Sajaysurya Ganesh +3
We present sktime -- a new scikit-learn compatible Python library with a unified interface for machine learning with time series. Time series data gives rise to various distinct bu…
Design choices for productive, secure, data-intensive research at scale in the cloud
Diego Arenas, Jon Atkins, Claire Austin +22
We present a policy and process framework for secure environments for productive data science research projects at scale, by combining prevailing data security threat and risk prof…
Machine Learning Automation Toolbox (MLaut)
Viktor Kazakov, Franz J. Király
In this paper we present MLaut (Machine Learning AUtomation Toolbox) for the python data science ecosystem. MLaut automates large-scale evaluation and benchmarking of machine learn…
Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness
Sebastian Vollmer, Bilal A. Mateen, Gergo Bohner +15
Machine learning (ML), artificial intelligence (AI) and other modern statistical methods are providing new opportunities to operationalize previously untapped and rapidly growing s…
NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature
Franz J Király, Bilal Mateen, Raphael Sonabend
Objective: To determine the completeness of argumentative steps necessary to conclude effectiveness of an algorithm in a sample of current ML/AI supervised learning literature. Dat…
Modeling outcomes of soccer matches
Alkeos Tsokos, Santhosh Narayanan, Ioannis Kosmidis +4
We compare various extensions of the Bradley-Terry model and a hierarchical Poisson log-linear model in terms of their performance in predicting the outcome of soccer matches (win,…
Probabilistic supervised learning
Frithjof Gressmann, Franz J. Király, Bilal Mateen +1
Predictive modelling and supervised learning are central to modern data science. With predictions from an ever-expanding number of supervised black-box strategies - e.g., kernel me…
Predictive Independence Testing, Predictive Conditional Independence Testing, and Predictive Graphical Modelling
Samuel Burkart, Franz J Király
Testing (conditional) independence of multivariate random variables is a task central to statistical inference and modelling in general - though unfortunately one for which to date…
Modelling Competitive Sports: Bradley-Terry-Élő Models for Supervised and On-Line Learning of Paired Competition Outcomes
Franz J. Király, Zhaozhi Qian
Prediction and modelling of competitive sports outcomes has received much recent attention, especially from the Bayesian statistics and machine learning communities. In the real wo…
Kernels for sequentially ordered data
Franz J Király, Harald Oberhauser
We present a novel framework for kernel learning with sequential data of any kind, such as time series, sequences of graphs, or strings. Our approach is based on signature features…
Phase retrieval using random cubatures and fusion frames of positive semidefinite matrices
Martin Ehler, Manuel Graef, Franz J. Kiraly
As a generalization of the standard phase retrieval problem, we seek to reconstruct symmetric rank-1 matrices from inner products with subclasses of positive semidefinite matrices.…
Prediction and Quantification of Individual Athletic Performance
Duncan A. J. Blythe, Franz J. Király
We provide scientific foundations for athletic performance prediction on an individual level, exposing the phenomenology of individual athletic running performance in the form of a…
Learning with Algebraic Invariances, and the Invariant Kernel Trick
Franz J. Király, Andreas Ziehe, Klaus-Robert Müller
When solving data analysis problems it is important to integrate prior knowledge and/or structural invariances. This paper contributes by a novel framework for incorporating algebr…
Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction
Duncan A. J. Blythe, Louis Theran, Franz Kiraly
This paper presents novel algorithms which exploit the intrinsic algebraic and combinatorial structure of the matrix completion task for estimating missing en- tries in the general…
Learning with Cross-Kernels and Ideal PCA
Franz J Király, Martin Kreuzer, Louis Theran
We describe how cross-kernel matrices, that is, kernel matrices between the data and a custom chosen set of `feature spanning points' can be used for learning. The main potential o…
Matroid Regression
Franz J Király, Louis Theran
We propose an algebraic combinatorial method for solving large sparse linear systems of equations locally - that is, a method which can compute single evaluations of the signal wit…
The Algebraic Approach to Phase Retrieval and Explicit Inversion at the Identifiability Threshold
Franz J Király, Martin Ehler
We study phase retrieval from magnitude measurements of an unknown signal as an algebraic estimation problem. Indeed, phase retrieval from rank-one and more general linear measurem…
Dual-to-kernel learning with ideals
Franz J. Király, Martin Kreuzer, Louis Theran
In this paper, we propose a theory which unifies kernel learning and symbolic algebraic methods. We show that both worlds are inherently dual to each other, and we use this duality…
Efficient Orthogonal Tensor Decomposition, with an Application to Latent Variable Model Learning
Franz J. Király
Decomposing tensors into orthogonal factors is a well-known task in statistics, machine learning, and signal processing. We study orthogonal outer product decompositions where the…