It enjoys model-free coverage guarantee regardless of the underlying distribution of the data.

NC phenomena occur with different loss functions.

This has motivated numerous studies aiming to reduce the variance and improve convergence of FL on non-IID data [6, 9, 14, 17, 19, 30]. On another note, constraints on communication resources and therefore on the number of clients that may participate in training additionally complicate implementation of FL schemes.

Since pioneering work of Hinton et al., knowledge distillation based on Kullback-Leibler Divergence (KL-Div) has been predominant, and recently its variants have

Despite the advancements in learning governing differential equations from observations of dynamical systems, data-driven methods are often unaware of fundamental physical laws, such as frame invariance.

This problem is particularly challenging when using gradient-based Markov Chain Monte Carlo (MCMC) algorithms due to diminishing gradients, which occurs when the tails of the target density decay at a slow (e.g.

This paper introduces a robust procedure by recasting the SIM into a pseudo-linear model with transformed responses. It relaxes the distributional conditions on random errors from sub-Gaussian to more general distributions and thus it is robust with substantial efficiency gain for heavy-tailed random errors.

Embeddings play a pivotal role across various disciplines, offering compact representations of complex data structures.