Explore a 25-minute IEEE conference talk on point location and active learning, focusing on learning halfspaces almost optimally. Delve into topics such as dual view labeling points, naive bounds, machine learning motivation, active learning solutions, and halfspaces in 2D. Examine membership queries, prior work, and two regimes of results. Investigate the overall strategy, learning with margin, vector scaling, isotropic transformation, and structure of the margin. Discover dimensionality reduction techniques, algorithm overview, verification processes, and open problems in this field presented by researchers from the University of California, San Diego.
Point Location and Active Learning - Learning Halfspaces Almost Optimally
Overview
Syllabus
Intro
Point Location
Dual View: Labeling Points
Naive Bounds
Motivation: Machine Learning
Solution: Active Learning
Problem: Halfspaces in 2D
Solution: Membership Queries
Prior Work
Two Regimes
Our Results (High probability regime)
Our Result (Zero-error regime)
Overall Strategy
Learning with Margin (Continued)
Vector Scaling
Isotropic Transformation
Structure of the Margin
Dimensionality Reduction: Example
Finding V
Algorithm Overview
Verification
Open Problems
Taught by
IEEE FOCS: Foundations of Computer Science
