When Your Big Data Seems Too Small: Accurate Inferences Beyond the Empirical Distribution - Part 2
Offered By: Institut Henri Poincaré via YouTube
Course Description
Overview
Explore advanced techniques for making accurate inferences about complex distributions when sample sizes are insufficient for empirical distributions to be reliable. Delve into three key problems: optimally de-noising empirical distributions to improve accuracy, estimating population spectra from limited high-dimensional data, and recovering low-rank approximations of probability matrices from observed count data. Learn about an instance-optimal learning algorithm for distribution approximation, methods for estimating unseen elements in larger samples, and applications to genomics. Examine approaches for accurately estimating covariance matrix eigenvalues in high-dimensional settings with limited samples. Investigate techniques for matrix recovery problems related to community detection and word embeddings. Gain insights from cutting-edge research on overcoming data limitations in statistical inference and machine learning applications.
Syllabus
When your big data seems too small: accurate inferences beyond the empirical distribution 2/2
Taught by
Institut Henri Poincaré
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