Explore recent advancements in high-dimensional learning through this comprehensive lecture by MIT's Ankur Moitra. Delve into parametric statistics, maximum likelihood estimation, and noise constraint techniques. Examine the effectiveness of empirical mean and variance, and understand the Folklore Theorem. Investigate robustness, hardness, and the price of robustness in estimation. Learn about the Robust Estimation Recipe and the WinWin Algorithm. Analyze relaxed distributional assumptions and their impact on robust estimation. Consider the error guarantee tendencies and the completeness of adversaries in gaussian mean estimation. Gain insights into adversary efficiency and improper learning in high-dimensional contexts.
Recent Progress in High-Dimensional Learning
Overview
Syllabus
Introduction
Parametric Statistics
Maximum likelihood estimation
How to constrain noise
Estimating parameters
Do empirical mean and empirical variance work
Folklore Theorem
Robustness and Hardness
Price of Robustness
Recent Results
Robust Estimation Recipe
WinWin Algorithm
Birds Eye View
O of epsilon
Relaxing Distributional Assumption
Robust Estimation
Conclusion
Does the error guarantee tend to O
Is there a sense that some adversaries are complete
gaussian mean estimation
adversary efficiency
improper learning
Taught by
Simons Institute
