Specifically, meta learning takes in a collection of tasks (datasets) sampled from an unknown distribution. Each task defines a learning problem with respect to an input dataset.

We propose MHM-GNN, an inductive unsupervised graph representation approach that combines jointk-node representations with energy-based models (hypergraph Markov networks) and GNNs.

Machine learning aims to generalize models trained on given datasets to make accurate predictions or decisions on new, unseen data (El Naqa and Murphy, 2015). The effectiveness of those models depends on the alignment between the training datasets and deployment environments (Quinonero-Candela et al.,2008).

The idea is to first develop a prediction model without concern for the downstream risk profile or robustness guarantee, and then utilize calibration (or recalibration) methods to quantify the uncertainty of the prediction.

Marginal likelihood (ML), sometimes calledevidence, is a central quantity in Bayesian learning as it measures how well a model can describe a particular dataset.

Marginal likelihood (ML), sometimes calledevidence, is a central quantity in Bayesian learning as it measures how well a model can describe a particular dataset.