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Large language models for forecasting and anomaly detection: A systematic literature review
This systematic literature review comprehensively examines the application of Large
Language Models (LLMs) in forecasting and anomaly detection, highlighting the current …
Language Models (LLMs) in forecasting and anomaly detection, highlighting the current …
Does learning require memorization? a short tale about a long tail
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 …
learning algorithms that (nearly) perfectly fit the training set and are known to fit well even …
Formal limitations on the measurement of mutual information
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 …
variational methods maximizing a lower bound. In this paper, we prove that serious …
Optimal prediction of the number of unseen species
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 …
endeavors. Its most popular formulation, introduced by Fisher et al.[Fisher RA, Corbet AS …
Mauve scores for generative models: Theory and practice
Generative artificial intelligence has made significant strides, producing text
indistinguishable from human prose and remarkably photorealistic images. Automatically …
indistinguishable from human prose and remarkably photorealistic images. Automatically …
Estimation of KL divergence: Optimal minimax rate
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 …
distributions P and Q is studied, under the assumption that the alphabet size k of the …
On universal features for high-dimensional learning and inference
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 …
dimensional data for inference tasks in settings involving learning. For such problems, we …
Instance-Optimal Private Density Estimation in the Wasserstein Distance
Estimating the density of a distribution from samples is a fundamental problem in statistics. In
many practical settings, the Wasserstein distance is an appropriate error metric for density …
many practical settings, the Wasserstein distance is an appropriate error metric for density …
Instance optimal learning of discrete distributions
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 …
distribution over a discrete support, output an approximation of the distribution that is as …
STADS: Software testing as species discovery
A fundamental challenge of software testing is the statistically well-grounded extrapolation
from program behaviors observed during testing. For instance, a security researcher who …
from program behaviors observed during testing. For instance, a security researcher who …