The proposed amortized Active Learning (AL) approach can be extended to handle more complex function spaces by incorporating alternative nonparametric models or hybrid models that combine different types of function approximators. For instance, one could integrate kernel methods beyond Gaussian processes, such as support vector machines with kernel tricks, or even deep learning models that utilize neural networks with various architectures (e.g., convolutional or recurrent networks) to capture complex patterns in data.
Additionally, the amortized AL framework could be adapted to leverage ensemble methods, where multiple models are trained simultaneously, and their predictions are combined to improve robustness and accuracy. This ensemble approach can help mitigate the limitations of individual models by capturing a broader range of function behaviors.
Moreover, incorporating domain knowledge into the model design can enhance the flexibility of the amortized AL method. For example, using physics-informed neural networks (PINNs) can allow the model to respect known physical laws while learning from data, thus improving generalization in scientific applications.
Lastly, the training pipeline can be modified to include a wider variety of simulated functions during the policy training phase, ensuring that the neural network policy is exposed to diverse function behaviors, which can help it generalize better to real-world scenarios.