Willem Waegeman | DeepAI

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∙ 09/15/2023

Heteroskedastic conformal regression

Conformal prediction, and split conformal prediction as a specific imple...

0 Nicolas Dewolf, et al. ∙

∙ 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...

0 Stijn Hawinkel, et al. ∙

∙ 01/30/2023

On Second-Order Scoring Rules for Epistemic Uncertainty Quantification

It is well known that accurate probabilistic predictors can be trained t...

0 Viktor Bengs, et al. ∙

∙ 11/08/2022

Hyperparameter optimization in deep multi-target prediction

As a result of the ever increasing complexity of configuring and fine-tu...

0 Dimitrios Iliadis, et al. ∙

∙ 05/20/2022

On Calibration of Ensemble-Based Credal Predictors

In recent years, several classification methods that intend to quantify ...

0 Thomas Mortier, et al. ∙

∙ 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...

0 Thomas Mortier, et al. ∙

∙ 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 ...

0 Viktor Bengs, et al. ∙

∙ 07/01/2021

Well-calibrated prediction intervals for regression problems

Over the last few decades, various methods have been proposed for estima...

0 Nicolas Dewolf, et al. ∙

∙ 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...

0 Dimitrios Iliadis, et al. ∙

∙ 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...

0 Eyke Hüllermeier, et al. ∙

∙ 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...

0 Thomas Mortier, et al. ∙

∙ 09/07/2018

Multi-Target Prediction: A Unifying View on Problems and Methods

Multi-target prediction (MTP) is concerned with the simultaneous predict...

0 Willem Waegeman, et al. ∙

∙ 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...

0 Michiel Stock, et al. ∙

∙ 06/19/2015

Spectral Analysis of Symmetric and Anti-Symmetric Pairwise Kernels

We consider the problem of learning regression functions from pairwise d...

0 Tapio Pahikkala, et al. ∙

∙ 05/17/2014

Identification of functionally related enzymes by learning-to-rank methods

Enzyme sequences and structures are routinely used in the biological sci...

0 Michiel Stock, et al. ∙

∙ 09/21/2012

Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data

In domains like bioinformatics, information retrieval and social network...

0 Tapio Pahikkala, et al. ∙

∙ 11/28/2011

A kernel-based framework for learning graded relations from data

Driven by a large number of potential applications in areas like bioinfo...

0 Willem Waegeman, et al. ∙

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