Calendar

Links to Slides, Relevant Chapters in [WM22] and Extra Reading, and Presenters

Lecture 1

Introduction to Low-Dimensional Models

John Wright

• Chapters 2-6, WM22

Lecture 2

Scalable Convex Optimization Methods for Low-Dimensional Structures

Qing Qu

• Chapter 8, WM22

Lecture 3

Learning Low-Dimensional Models via Nonconvex Optimization

Yuqian Zhang

• Chapters 7, 9, WM22
• Survey: “From Symmetry to Geometry: Tractable Nonconvex Problems”

Lecture 4

Learning Low-Dimensional Structures via Deep Networks

Sam Buchanan, Zhihui Zhu

• Chapter 16, WM22
• Deep Networks and the Multiple Manifold Problem
• Deep Networks Provably Classify Data on Curves
• A Geometric Analysis of Neural Collapse with Unconstrained Features
• On the Optimization Landscape of Neural Collapse under MSE Loss: Global Optimality with Unconstrained Features
• Robust Training under Label Noise by Over-parameterization

Lecture 5

Design Deep Networks for Pursuing Low-Dimensional Structures

Yi Ma

• Chapter 16, WM22
• Slides for ICASSP 2022 plenary lecture
• ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction
• CTRL: Closed-Loop Transcription to an LDR via Minimaxing Rate Reduction