This paper studies the problem of entropy identity testing: given sample access to a distribution p and a fully described distribution q (both discrete distributions over a domain of size k), and the promise that either p = q or |H (p) H (q)| ε, where H () denotes the Shannon entropy, a tester needs to distinguish between the two cases with high probability.

In question-answering scenarios, humans can assess whether the available information is sufficient and seek additional information if necessary, rather than providing a forced answer.

FL further makes efforts to adapt real-world scenarios, i.e., adapting

We develop EigenVI, an eigenvalue-based approach for black-box variational inference (BBVI).

Independent Component Analysis (ICA) was introduced in the 1980's as a model

Federated Learning (FL) iscommonly used tocollaborativelytrain models with privacypreservation. Specifically,AdaSCP evaluates the importance of parameters with the gradients in dominant timesteps of the diffusion model.