Explore fundamental concepts in distributed learning and statistical inference through this IEEE conference talk. Delve into protocols, models, and general formulations for distributed settings. Examine two sets of distributions, focusing on discrete distributions and those over high-dimensional domains. Gain insights into the challenges and approaches in constrained statistical inference, with references provided for further study.
Cookbook Lower Bounds for Statistical Inference in Distributed and Constrained Settings - Part 1
Overview
Syllabus
Introduction
Distributed Learning and Testing
Example
Model
Protocols
Models
General formulation
Two sets of distributions
Discrete distributions
Distribution over a highdimensional domain
Discrete distribution
References
Outline
Taught by
IEEE FOCS: Foundations of Computer Science
Tags
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