Discrete Optimization-Aided Structured Learning at Scale - IPAM at UCLA

Explore discrete optimization-aided structured learning at scale in this 56-minute lecture presented by Rahul Mazumder from MIT at IPAM's Artificial Intelligence and Discrete Optimization Workshop. Delve into algorithmic aspects, statistical setups, canonical and optimal estimators, and various approaches to feature selection and learning trees. Gain insights into performance metrics and engage with thought-provoking questions in this comprehensive exploration of cutting-edge machine learning techniques.

Intro
Outline
Algorithmic Aspects
Statistical Setup
canonical estimators
Optimal estimators
Approach
Performance
Approaches
Feature Selection
Questions
Learning Trees