Neural processes (NPs) formulate exchangeable stochastic processes and are promising models for meta learning that do not require gradient updates during thetestingphase.

Last, weprovide numerical experiments toillustrate our results and endorse our intuitions.

In this paper, we will give the non-asymptotic convergence rate of the prediction error in high-dimensional adversarial training.

Compared with Eq. 25 and Eq. 29, we find We can see the approximate solutions. The tasks are binary classification. We use the default train/val/test split with ratio 8:1:1. The experiments are easily influenced by the parameter initialization. The AUC-ROC results are reported in Tab. 2. H.4 We use the official splits and evaluation is conducted on validation set.

Thus a gap has arisen between theory and practice.

Models like LASSO and ridge regression are extensively used in practice due to their interpretability, ease of use, and strong theoretical guarantees.