We introduce a natural and mild restriction on the meta-level combination functions of the local trials.

Fréchet regression has emerged as a promising approach for regression analysis involving non-Euclidean response variables.

TPT does not explicitly align the pre-trained CLIP to become aware of the test sample distribution. For the effective test-time adaptation of V -L foundation models, it is crucial to bridge the distribution gap between the pre-training dataset and the downstream evaluation set for high zero-shot generalization.

Knockoffs are a popular statistical tool for conditional variable selection in high dimension.

Achieving such guarantees generally requires the injection of noise, either directly into parameter estimates or into the estimation process.

However, previous NF-based methods forcibly transform the distribution of all features into a single distribution (e.g., unit normal distribution), even when the features can have locally distinct semantic information and thus follow different

The fundamental problem of non-parametric two-sample testing consists in detecting the difference between any two distributions having access only to samples from these.