This systematic literature review comprehensively examines the application of Large
Language Models (LLMs) in forecasting and anomaly detection, highlighting the current …

State-of-the-art results on image recognition tasks are achieved using over-parameterized
learning algorithms that (nearly) perfectly fit the training set and are known to fit well even …

Measuring mutual information from finite data is difficult. Recent work has considered
variational methods maximizing a lower bound. In this paper, we prove that serious …

Estimating the number of unseen species is an important problem in many scientific
endeavors. Its most popular formulation, introduced by Fisher et al.[Fisher RA, Corbet AS …

Generative artificial intelligence has made significant strides, producing text
indistinguishable from human prose and remarkably photorealistic images. Automatically …

We consider the problem of identifying universal low-dimensional features from high-
dimensional data for inference tasks in settings involving learning. For such problems, we …

The problem of estimating the Kullback-Leibler divergence D (P∥ Q) between two unknown
distributions P and Q is studied, under the assumption that the alphabet size k of the …

Consider a finite sample from an unknown distribution over a countable alphabet. The
occupancy probability (OP) refers to the total probability of symbols that appear exactly k …

We consider the following basic learning task: given independent draws from an unknown
distribution over a discrete support, output an approximation of the distribution that is as …

A fundamental challenge of software testing is the statistically well-grounded extrapolation
from program behaviors observed during testing. For instance, a security researcher who …