research
∙
09/15/2023
Heteroskedastic conformal regression
Conformal prediction, and split conformal prediction as a specific imple...
share
research
∙
02/10/2023
The out-of-sample R^2: estimation and inference
Out-of-sample prediction is the acid test of predictive models, yet an i...
research
∙
01/30/2023
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification
It is well known that accurate probabilistic predictors can be trained t...
research
∙
11/08/2022
Hyperparameter optimization in deep multi-target prediction
As a result of the ever increasing complexity of configuring and fine-tu...
research
∙
05/20/2022
On Calibration of Ensemble-Based Credal Predictors
In recent years, several classification methods that intend to quantify ...
research
∙
03/13/2022
Set-valued prediction in hierarchical classification with constrained representation complexity
Set-valued prediction is a well-known concept in multi-class classificat...
research
∙
03/11/2022
On the Difficulty of Epistemic Uncertainty Quantification in Machine Learning: The Case of Direct Uncertainty Estimation through Loss Minimisation
Uncertainty quantification has received increasing attention in machine ...
research
∙
07/01/2021
Well-calibrated prediction intervals for regression problems
Over the last few decades, various methods have been proposed for estima...
research
∙
04/19/2021
Automated problem setting selection in multi-target prediction with AutoMTP
Algorithm Selection (AS) is concerned with the selection of the best-sui...
research
∙
10/21/2019
Aleatoric and Epistemic Uncertainty in Machine Learning: A Tutorial Introduction
The notion of uncertainty is of major importance in machine learning and...
research
∙
06/19/2019
Efficient Algorithms for Set-Valued Prediction in Multi-Class Classification
In cases of uncertainty, a multi-class classifier preferably returns a s...
research
∙
09/07/2018
Multi-Target Prediction: A Unifying View on Problems and Methods
Multi-target prediction (MTP) is concerned with the simultaneous predict...
research
∙
03/05/2018
A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression
Many machine learning problems can be formulated as predicting labels fo...
research
∙
06/19/2015
Spectral Analysis of Symmetric and Anti-Symmetric Pairwise Kernels
We consider the problem of learning regression functions from pairwise d...
research
∙
05/17/2014
Identification of functionally related enzymes by learning-to-rank methods
Enzyme sequences and structures are routinely used in the biological sci...
research
∙
09/21/2012
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data
In domains like bioinformatics, information retrieval and social network...
research
∙
11/28/2011