Public articles linked to the same research event.
arXiv The work introduces a first statistical framework for epistemic uncertainty in node classification, comprising an information-growth experimental protocol and a consistency criterion, and uses projective graph data-generating processes to ensure nested graphs are coherent observations of the same process; the analysis shows that graph evidential deep learning (EDL) methods retain non-vanishing epistemic uncertainty at the population optimum and regulate epistemic and aleatoric uncertainty externally through hyperparameters, thus failing consistency, whereas graph bootstrap ensembles (GB-Ens) capture both data and procedural uncertainty through graph resampling and randomized training and exhibit epistemic uncertainty contraction beyond standard deep ensembles under the same protocol.
The work introduces a first statistical framework for epistemic uncertainty in node classification, comprising an information-growth experimental protocol and a consistency criterion, and uses projective graph data-generating processes to ensure nested graphs are coherent observations of the same process; the analysis shows that graph evidential deep learning (EDL) methods retain non-vanishing epistemic uncertainty at the population optimum and regulate epistemic and aleatoric uncertainty externally through hyperparameters, thus failing consistency, whereas graph bootstrap ensembles (GB-Ens) capture both data and procedural uncertainty through graph resampling and randomized training and exhibit epistemic uncertainty contraction beyond standard deep ensembles under the same protocol.
The work introduces a first statistical framework for epistemic uncertainty in node classification, comprising an information-growth experimental protocol and a consistency criterion, and uses projective graph data-generating processes to ensure nested graphs are coherent observations of the same process; the analysis shows that graph evidential deep learning (EDL) methods retain non-vanishing epistemic uncertainty at the population optimum and regulate epistemic and aleatoric uncertainty externally through hyperparameters, thus failing consistency, whereas graph bootstrap ensembles (GB-Ens) capture both data and procedural uncertainty through graph resampling and randomized training and exhibit epistemic uncertainty contraction beyond standard deep ensembles under the same protocol.
The work introduces a first statistical framework for epistemic uncertainty in node classification, comprising an information-growth experimental protocol and a consistency criterion, and uses projective graph data-generating processes to ensure nested graphs are coherent observations of the same process; the analysis shows that graph evidential deep learning (EDL) methods retain non-vanishing epistemic uncertainty at the population optimum and regulate epistemic and aleatoric uncertainty externally through hyperparameters, thus failing consistency, whereas graph bootstrap ensembles (GB-Ens) capture both data and procedural uncertainty through graph resampling and randomized training and exhibit epistemic uncertainty contraction beyond standard deep ensembles under the same protocol.