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On Second-Order Scoring Rules for Epistemic Uncertainty Quantification
Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
It is well known that accurate probabilistic predictors can be trained through empirical risk minimisation with proper scoring rules as loss functions. While such learners capture…
Hyperparameter optimization in deep multi-target prediction
Dimitrios Iliadis, Marcel Wever, Bernard De Baets +1
As a result of the ever increasing complexity of configuring and fine-tuning machine learning models, the field of automated machine learning (AutoML) has emerged over the past dec…
Set-valued prediction in hierarchical classification with constrained representation complexity
Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczyński +1
Set-valued prediction is a well-known concept in multi-class classification. When a classifier is uncertain about the class label for a test instance, it can predict a set of class…
Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation
Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
Uncertainty quantification has received increasing attention in machine learning in the recent past. In particular, a distinction between aleatoric and epistemic uncertainty has be…
Multi-target prediction for dummies using two-branch neural networks
Dimitrios Iliadis, Bernard De Baets, Willem Waegeman
Multi-target prediction (MTP) serves as an umbrella term for machine learning tasks that concern the simultaneous prediction of multiple target variables. Classical instantiations…
Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods
Eyke Hüllermeier, Willem Waegeman
The notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology. In line with the statistical tradition, uncertai…
Efficient Set-Valued Prediction in Multi-Class Classification
Thomas Mortier, Marek Wydmuch, Krzysztof Dembczyński +2
In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes instead of predicting a single class label with little guarantee. More precisely, th…
Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method
Michiel Stock, Tapio Pahikkala, Antti Airola +2
Pairwise learning or dyadic prediction concerns the prediction of properties for pairs of objects. It can be seen as an umbrella covering various machine learning problems such as…
Spectral Analysis of Symmetric and Anti-Symmetric Pairwise Kernels
Tapio Pahikkala, Markus Viljanen, Antti Airola +1
We consider the problem of learning regression functions from pairwise data when there exists prior knowledge that the relation to be learned is symmetric or anti-symmetric. Such p…
A two-step learning approach for solving full and almost full cold start problems in dyadic prediction
Tapio Pahikkala, Michiel Stock, Antti Airola +3
Dyadic prediction methods operate on pairs of objects (dyads), aiming to infer labels for out-of-sample dyads. We consider the full and almost full cold start problem in dyadic pre…
Identification of functionally related enzymes by learning-to-rank methods
Michiel Stock, Thomas Fober, Eyke Hüllermeier +6
Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually de…
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data
Tapio Pahikkala, Antti Airola, Michiel Stock +2
In domains like bioinformatics, information retrieval and social network analysis, one can find learning tasks where the goal consists of inferring a ranking of objects, conditione…