The significant heterogeneity poses higher requirements foralgorithm robustness.

Alldatausedispublic.] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they werechosen)? A.1 TrainingDetails In our experiments, the classifierfθ is a 8-layer MLP with 128 hidden dimensions per layer.

Although deep learning programs havedemonstrated strong performance on novel applications, they sacrifice many of the functionalities of traditional software programs.

Figure 11: RecBeerenvironments: Each year serves as a different environment, whose affect is expressed through differing correlations between beer types and user choices.

To address these issues, we introduce a novel Bayesian meta-learning approach called meta-variational dropout (MetaVD).

Whether when planning our paths home from the office or from a hotel to an airport in an unfamiliar city, we typically focus on a small subset of relevant variables,e.g. the changeinposition orthepresence oftraffic.

Many industries now deploy high-fidelity simulators (digital twins) to represent physical systems, yet their parameters must be calibrated to match the true system. This motivated the construction of simulation-driven parameter estimators, built by generating synthetic observations for sampled parameter values and learning a supervised mapping from observations to parameters. However, when the true parameters lie outside the sampled range, predictions suffer from an out-of-distribution (OOD) error. This paper introduces a fine-tuning approach for the Two-Stage estimator that mitigates OOD effects and improves accuracy. The effectiveness of the proposed method is verified through numerical simulations.