Search Results for author:
Found 11 papers, 7 papers with code
Interpretable Machine Learning for TabPFN
The recently developed Prior-Data Fitted Networks (PFNs) have shown very promising results for applications in low-data regimes.
Second-Order Uncertainty Quantification: Variance-Based Measures
Uncertainty quantification is a critical aspect of machine learning models, providing important insights into the reliability of predictions and aiding the decision-making process in real-world applications.
An Online Bootstrap for Time Series
In this paper, we propose a novel bootstrap method that is designed to account for data dependencies and can be executed online, making it particularly suitable for real-time applications.
Statistical Foundations of Prior-Data Fitted Networks
The pre-trained model is then used to infer class probabilities in-context on fresh training sets with arbitrary size and distribution.
Approximately Bayes-Optimal Pseudo Label Selection
We derive this selection criterion by proving Bayes optimality of the posterior predictive of pseudo-samples.
Explaining predictive models using Shapley values and non-parametric vine copulas
In this paper we propose two new approaches for modelling the dependence between the features.
Copula-based synthetic data augmentation for machine-learning emulators
Can we improve machine-learning (ML) emulators with synthetic data?
Stationary vine copula models for multivariate time series
Vine copulas are graphical models for the dependence and can conveniently capture both types of dependence in the same model.
Methodology
Asymptotic analysis of the jittering kernel density estimator
We give an in-depth analysis of the jittering kernel density estimator, which reveals several appealing properties.
Methodology
A generic approach to nonparametric function estimation with mixed data
In this case, any estimator developed in a purely continuous framework extends naturally to the mixed data setting.
Methodology
Generalized Additive Models for Pair-Copula Constructions
Pair-copula constructions are flexible dependence models that use bivariate copulas as building blocks.
Methodology
