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Found 23 papers, 2 papers with code
Designing Machine Learning Toolboxes: Concepts, Principles and Patterns
In particular, we develop a conceptual model for the AI/ML domain, with a new type system, called scientific types, at its core.
mlr3proba: An R Package for Machine Learning in Survival Analysis
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.
Kernels for time series with irregularly-spaced multivariate observations
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".
sktime: A Unified Interface for Machine Learning with Time Series
We present sktime -- a new scikit-learn compatible Python library with a unified interface for machine learning with time series.
Machine Learning Automation Toolbox (MLaut)
In this paper we present MLaut (Machine Learning AUtomation Toolbox) for the python data science ecosystem.
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 statistical methods are providing new opportunities to operationalize previously untapped and rapidly growing sources of data for patient benefit.
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 necessary to conclude effectiveness of an algorithm in a sample of current ML/AI supervised learning literature.
Modeling outcomes of soccer matches
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.
Probabilistic supervised learning
Predictive modelling and supervised learning are central to modern data science.
Predictive Independence Testing, Predictive Conditional Independence Testing, and Predictive Graphical Modelling
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.
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 much recent attention, especially from the Bayesian statistics and machine learning communities.
Machine Learning in Falls Prediction; A cognition-based predictor of falls for the acute neurological in-patient population
Conclusion: Predictive modelling has identified a simple yet powerful machine learning prediction strategy based on a single clinical test, the Trail test.
Kernels for sequentially ordered data
We present a novel framework for kernel learning with sequential data of any kind, such as time series, sequences of graphs, or strings.
Prediction and Quantification of Individual Athletic Performance
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.
Learning with Algebraic Invariances, and the Invariant Kernel Trick
When solving data analysis problems it is important to integrate prior knowledge and/or structural invariances.
Learning with Cross-Kernels and Ideal PCA
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.
Matroid Regression
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.
The Algebraic Approach to Phase Retrieval and Explicit Inversion at the Identifiability Threshold
We study phase retrieval from magnitude measurements of an unknown signal as an algebraic estimation problem.
Dual-to-kernel learning with ideals
In this paper, we propose a theory which unifies kernel learning and symbolic algebraic methods.
Efficient Orthogonal Tensor Decomposition, with an Application to Latent Variable Model Learning
Decomposing tensors into orthogonal factors is a well-known task in statistics, machine learning, and signal processing.
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 entries of incomplete and noisy entries.
Coherence and sufficient sampling densities for reconstruction in compressed sensing
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.
The Algebraic Combinatorial Approach for Low-Rank Matrix Completion
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.
