1.7k citations · 2.1k across the 113 of their papers we have counts for
13 papers · 1 filter
Credal Prediction based on Relative Likelihood
Timo Löhr, Paul Hofman, Felix Mohr +1
Predictions in the form of sets of probability distributions, so-called credal sets, provide a suitable means to represent a learner's epistemic uncertainty. In this paper, we prop…
Optimal Conformal Prediction under Epistemic Uncertainty
Alireza Javanmardi, Soroush H. Zargarbashi, Santo M. A. R. Thies +3
Conformal prediction (CP) is a widely used frequentist framework to quantify uncertainty by constructing prediction sets with user-specified marginal coverage guarantees. In practi…
Conformal Prediction Regions are Imprecise Highest Density Regions
Michele Caprio, Yusuf Sale, Eyke Hüllermeier
Recently, Cella and Martin proved how, under an assumption called consonance, a credal set (i.e. a closed and convex set of probabilities) can be derived from the conformal transdu…
Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity
Thomas Mortier, Alireza Javanmardi, Yusuf Sale +2
Conformal prediction has emerged as a widely used framework for constructing valid prediction sets in classification and regression tasks. In this work, we extend the split conform…
Conformalized Credal Set Predictors
Alireza Javanmardi, David Stutz, Eyke Hüllermeier
Credal sets are sets of probability distributions that are considered as candidates for an imprecisely known ground-truth distribution. In machine learning, they have recently attr…
Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning
Amirhossein Vahidi, Lisa Wimmer, Hüseyin Anil Gündüz +3
Ensembling a neural network is a widely recognized approach to enhance model performance, estimate uncertainty, and improve robustness in deep supervised learning. However, deep en…
A Novel Bayes' Theorem for Upper Probabilities
Michele Caprio, Yusuf Sale, Eyke Hüllermeier +1
In their seminal 1990 paper, Wasserman and Kadane establish an upper bound for the Bayes' posterior probability of a measurable set , when the prior lies in a class of probabili…
On the Calibration of Probabilistic Classifier Sets
Thomas Mortier, Viktor Bengs, Eyke Hüllermeier +2
Multi-class classification methods that produce sets of probabilistic classifiers, such as ensemble learning methods, are able to model aleatoric and epistemic uncertainty. Aleator…
Multi-Target Prediction: A Unifying View on Problems and Methods
Willem Waegeman, Krzysztof Dembczynski, Eyke Huellermeier
Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has develope…
Deep Architectures for Learning Context-dependent Ranking Functions
Karlson Pfannschmidt, Pritha Gupta, Eyke Hüllermeier
Object ranking is an important problem in the realm of preference learning. On the basis of training data in the form of a set of rankings of objects, which are typically represent…
Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening
Mohsen Ahmadi Fahandar, Eyke Hüllermeier, Inés Couso
We consider the problem of statistical inference for ranking data, specifically rank aggregation, under the assumption that samples are incomplete in the sense of not comprising al…
Learning to Rank based on Analogical Reasoning
Mohsen Ahmadi Fahandar, Eyke Hüllermeier
Object ranking or "learning to rank" is an important problem in the realm of preference learning. On the basis of training data in the form of a set of rankings of objects represen…
On the Bayes-optimality of F-measure maximizers
Willem Waegeman, Krzysztof Dembczynski, Arkadiusz Jachnik +2
The F-measure, which has originally been introduced in information retrieval, is nowadays routinely used as a performance metric for problems such as binary classification, multi-l…