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When Your Big Data Seems Too Small: Accurate Inferences Beyond the Empirical Distribution - Part 2

Offered By: Institut Henri Poincaré via YouTube

Tags

Statistical Inference Courses Data Analysis Courses Machine Learning Courses Probability Theory Courses Word Embeddings Courses Low-Rank Approximation Courses

Course Description

Overview

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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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