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. Whilst many R …
the number of machine-learning interfaces for implementing these models. Whilst many R …
A data-driven market simulator for small data environments
H Buehler, B Horvath, T Lyons, IP Arribas… - arXiv preprint arXiv …, 2020 - arxiv.org
Neural network based data-driven market simulation unveils a new and flexible way of
modelling financial time series without imposing assumptions on the underlying stochastic …
modelling financial time series without imposing assumptions on the underlying stochastic …
MLJ: A Julia package for composable machine learning
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 …
interface for interacting with machine learning models written in Julia and other languages. It …
Learning optimal representations with the decodable information bottleneck
We address the question of characterizing and finding optimal representations for
supervised learning. Traditionally, this question has been tackled using the Information …
supervised learning. Traditionally, this question has been tackled using the Information …
A theoretical and methodological framework for machine learning in survival analysis: Enabling transparent and accessible predictive modelling on right-censored …
REB Sonabend - 2021 - discovery.ucl.ac.uk
Survival analysis is an important field of Statistics concerned with mak-ing time-to-event
predictions with 'censored'data. Machine learning, specifically supervised learning, is the …
predictions with 'censored'data. Machine learning, specifically supervised learning, is the …
Proper scoring rules, gradients, divergences, and entropies for paths and time series
P Bonnier, H Oberhauser - Bayesian Analysis, 2024 - projecteuclid.org
We study scoring rules to assess forecasts of trajectories, given either in discrete or
continuous time. Our approach leverages the statistical framework of proper scoring rules …
continuous time. Our approach leverages the statistical framework of proper scoring rules …
Path classification by stochastic linear recurrent neural networks
We investigate the functioning of a classifying biological neural network from the perspective
of statistical learning theory, modelled, in a simplified setting, as a continuous-time …
of statistical learning theory, modelled, in a simplified setting, as a continuous-time …
Predictive independence testing, predictive conditional independence testing, and predictive graphical modelling
S Burkart, FJ Király - arXiv preprint arXiv:1711.05869, 2017 - arxiv.org
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 …
statistical inference and modelling in general-though unfortunately one for which to date …
Psychometric analysis of the Glasgow Coma Scale and its sub-scale scores in a national retrospective cohort of patients with traumatic injuries
Objectives To determine the psychometric validity, using Rasch analysis, of summing the
three constituent parts of the Glasgow Coma Scale (GCS). Design National (registry-based) …
three constituent parts of the Glasgow Coma Scale (GCS). Design National (registry-based) …
Applications of stochastic analysis and algebra to machine learning
POM Bonnier - 2023 - ora.ox.ac.uk
In this thesis we consider the application of tools from stochastic analysis and algebra to
statistics and machine learning. Most of these tools are different forms of what has become …
statistics and machine learning. Most of these tools are different forms of what has become …