Robust estimation and outlier detection play a critical role in modern data analysis, particularly when dealing with high-dimensional datasets. In such contexts, classical statistical methods often ...

Filling gaps in data sets or identifying outliers – that’s the domain of the machine learning algorithm TabPFN, developed by a team led by Prof. Dr. Frank Hutter from the University of Freiburg. This ...

SYDNEY—As central bankers in many major economies puzzle over the forceful re-emergence of inflation and what to do about it, the Reserve Bank of Australia looks pretty chill by comparison. Australian ...

Entropic outlier sparsification (EOS) is proposed as a cheap and robust computational strategy for learning in the presence of data anomalies and outliers. EOS dwells on the derived analytic solution ...

The first half of the year has seen a domino effect of states passing data privacy laws one after the other. While some have aligned in their way of protecting consumers' privacy, others have taken ...

A team has developed a new method that facilitates and improves predictions of tabular data, especially for small data sets with fewer than 10,000 data points. The new AI model TabPFN is trained on ...