Franz J Király

research

∙ 01/27/2021

Periodic seismicity detection without declustering

Any periodic variations of earthquake occurrence rates in response to sm...

Timothy Park, et al. ∙

research

∙ 01/13/2021

Designing Machine Learning Toolboxes: Concepts, Principles and Patterns

Machine learning (ML) and AI toolboxes such as scikit-learn or Weka are ...

Franz J Király, et al. ∙

research

∙ 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...

Raphael Sonabend, et al. ∙

research

∙ 04/18/2020

Kernels for time series with irregularly-spaced multivariate observations

Time series are an interesting frontier for kernel-based methods, for th...

Ahmed Guecioueur, et al. ∙

research

∙ 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...

Markus Löning, et al. ∙

research

∙ 01/11/2019

Machine Learning Automation Toolbox (MLaut)

In this paper we present MLaut (Machine Learning AUtomation Toolbox) for...

Viktor Kazakov, et al. ∙

research

∙ 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...

Sebastian Vollmer, et al. ∙

research

∙ 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...

Franz J Király, et al. ∙

research

∙ 07/04/2018

Modeling outcomes of soccer matches

We compare various extensions of the Bradley-Terry model and a hierarchi...

Alkeos Tsokos, et al. ∙

research

∙ 01/02/2018

Probabilistic supervised learning

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

Frithjof Gressmann, et al. ∙

research

∙ 11/16/2017

Predictive Independence Testing, Predictive Conditional Independence Testing, and Predictive Graphical Modelling

Testing (conditional) independence of multivariate random variables is a...

Samuel Burkart, et al. ∙

research

∙ 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...

Franz J Király, et al. ∙

research

∙ 01/29/2016

Kernels for sequentially ordered data

We present a novel framework for kernel learning with sequential data of...

Franz J Király, et al. ∙

research

∙ 05/05/2015

Prediction and Quantification of Individual Athletic Performance

We provide scientific foundations for athletic performance prediction on...

Duncan A. J. Blythe, et al. ∙

research

∙ 11/28/2014

Learning with Algebraic Invariances, and the Invariant Kernel Trick

When solving data analysis problems it is important to integrate prior k...

Franz J Király, et al. ∙

research

∙ 03/04/2014

Matroid Regression

We propose an algebraic combinatorial method for solving large sparse li...

Franz J Király, et al. ∙

research

∙ 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...

Franz J Király, et al. ∙

research

∙ 02/01/2014

Dual-to-kernel learning with ideals

In this paper, we propose a theory which unifies kernel learning and sym...

Franz J Király, et al. ∙

research

∙ 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...

Franz J Király, et al. ∙

research

∙ 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...

Franz J Király, et al. ∙

research

∙ 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...

Franz J Király, et al. ∙

research

∙ 11/30/2012

Approximate Rank-Detecting Factorization of Low-Rank Tensors

We present an algorithm, AROFAC2, which detects the (CP-)rank of a degre...

Franz J Király, et al. ∙

research

∙ 11/17/2012

The Algebraic Combinatorial Approach for Low-Rank Matrix Completion

We present a novel algebraic combinatorial view on low-rank matrix compl...

Franz J Király, et al. ∙

research

∙ 08/06/2011

Algebraic Geometric Comparison of Probability Distributions

We propose a novel algebraic framework for treating probability distribu...

Franz J Király, et al. ∙

Franz J Király

Featured Co-authors

Klaus-Robert Müller

Bernd Bischl

Chris Holmes

Mihai Cucuringu

Ryota Tomioka

Anthony Bagnall

Gianluca Baio

Harald Oberhauser

Andreas Bender

Rayid Ghani

Ioannis Kosmidis

Periodic seismicity detection without declustering

Designing Machine Learning Toolboxes: Concepts, Principles and Patterns

mlr3proba: An R Package for Machine Learning in Survival Analysis

Kernels for time series with irregularly-spaced multivariate observations

sktime: A Unified Interface for Machine Learning with Time Series

Machine Learning Automation Toolbox (MLaut)

Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness

NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature

Modeling outcomes of soccer matches

Probabilistic supervised learning

Predictive Independence Testing, Predictive Conditional Independence Testing, and Predictive Graphical Modelling

Modelling Competitive Sports: Bradley-Terry-Élő Models for Supervised and On-Line Learning of Paired Competition Outcomes

Kernels for sequentially ordered data

Prediction and Quantification of Individual Athletic Performance

Learning with Algebraic Invariances, and the Invariant Kernel Trick

Matroid Regression

The Algebraic Approach to Phase Retrieval and Explicit Inversion at the Identifiability Threshold

Dual-to-kernel learning with ideals

Efficient Orthogonal Tensor Decomposition, with an Application to Latent Variable Model Learning

Obtaining error-minimizing estimates and universal entry-wise error bounds for low-rank matrix completion

Coherence and sufficient sampling densities for reconstruction in compressed sensing

Approximate Rank-Detecting Factorization of Low-Rank Tensors

The Algebraic Combinatorial Approach for Low-Rank Matrix Completion

Algebraic Geometric Comparison of Probability Distributions