no code implementations • 13 Jan 2021 • Franz J. Király, Markus Löning, Anthony Blaom, Ahmed Guecioueur, Raphael Sonabend

In particular, we develop a conceptual model for the AI/ML domain, with a new type system, called scientific types, at its core.

no code implementations • 18 Aug 2020 • Raphael Sonabend, Franz J. Király, Andreas Bender, Bernd Bischl, Michel Lang

As machine learning has become increasingly popular over the last few decades, so too has the number of machine learning interfaces for implementing these models.

no code implementations • 18 Apr 2020 • Ahmed Guecioueur, Franz J. Király

In this manuscript, we show that a "series kernel" that is general enough to represent irregularly-spaced multivariate time series may be built out of well-known "vector kernels".

no code implementations • 17 Sep 2019 • Markus Löning, Anthony Bagnall, Sajaysurya Ganesh, Viktor Kazakov, Jason Lines, Franz J. Király

We present sktime -- a new scikit-learn compatible Python library with a unified interface for machine learning with time series.

1 code implementation • 11 Jan 2019 • Viktor Kazakov, Franz J. Király

In this paper we present MLaut (Machine Learning AUtomation Toolbox) for the python data science ecosystem.

no code implementations • 21 Dec 2018 • Sebastian Vollmer, Bilal A. Mateen, Gergo Bohner, Franz J. Király, Rayid Ghani, Pall Jonsson, Sarah Cumbers, Adrian Jonas, Katherine S. L. McAllister, Puja Myles, David Granger, Mark Birse, Richard Branson, Karel GM Moons, Gary S Collins, John P. A. Ioannidis, Chris Holmes, Harry Hemingway

Machine learning (ML), artificial intelligence (AI) and other modern statistical methods are providing new opportunities to operationalize previously untapped and rapidly growing sources of data for patient benefit.

no code implementations • 18 Dec 2018 • 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.

no code implementations • 4 Jul 2018 • Alkeos Tsokos, Santhosh Narayanan, Ioannis Kosmidis, Gianluca Baio, Mihai Cucuringu, Gavin Whitaker, Franz J. Király

The parameters of the Bradley-Terry extensions are estimated by maximizing the log-likelihood, or an appropriately penalized version of it, while the posterior densities of the parameters of the hierarchical Poisson log-linear model are approximated using integrated nested Laplace approximations.

no code implementations • 2 Jan 2018 • Frithjof Gressmann, Franz J. Király, Bilal Mateen, Harald Oberhauser

Predictive modelling and supervised learning are central to modern data science.

1 code implementation • 16 Nov 2017 • Samuel Burkart, Franz J. Király

As a practical implementation of this link between the two workflows, we present a python package 'pcit', which implements our novel multivariate and conditional independence tests, interfacing the supervised learning API of the scikit-learn package.

no code implementations • 27 Jan 2017 • 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.

no code implementations • 5 Jul 2016 • Bilal A. Mateen, Matthias Bussas, Catherine Doogan, Denise Waller, Alessia Saverino, Franz J. Király, E Diane Playford

Conclusion: Predictive modelling has identified a simple yet powerful machine learning prediction strategy based on a single clinical test, the Trail test.

no code implementations • 29 Jan 2016 • 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.

no code implementations • 5 May 2015 • 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 low-rank model dominated by an individual power law.

no code implementations • 28 Nov 2014 • Franz J. Király, Andreas Ziehe, Klaus-Robert Müller

When solving data analysis problems it is important to integrate prior knowledge and/or structural invariances.

no code implementations • 10 Jun 2014 • 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.

no code implementations • 4 Mar 2014 • Franz J. Király, Louis Theran

At the heart of our approach is the so-called regression matroid, a combinatorial object associated to sparsity patterns, which allows to replace inversion of the large matrix with the inversion of a kernel matrix that is constant size.

no code implementations • 17 Feb 2014 • Franz J. Király, Martin Ehler

We study phase retrieval from magnitude measurements of an unknown signal as an algebraic estimation problem.

no code implementations • 1 Feb 2014 • Franz J. Király, Martin Kreuzer, Louis Theran

In this paper, we propose a theory which unifies kernel learning and symbolic algebraic methods.

no code implementations • 12 Sep 2013 • Franz J. Király

Decomposing tensors into orthogonal factors is a well-known task in statistics, machine learning, and signal processing.

no code implementations • 21 Feb 2013 • Franz J. Király, Louis Theran

We propose a general framework for reconstructing and denoising single entries of incomplete and noisy entries.

no code implementations • 12 Feb 2013 • Franz J. Király, Louis Theran

We give a new, very general, formulation of the compressed sensing problem in terms of coordinate projections of an analytic variety, and derive sufficient sampling rates for signal reconstruction.

no code implementations • 17 Nov 2012 • Franz J. Király, Louis Theran, Ryota Tomioka

We present a novel algebraic combinatorial view on low-rank matrix completion based on studying relations between a few entries with tools from algebraic geometry and matroid theory.

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