1. From Signal Processing to Machine Learning
New Activation Patterns,
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2. Outline
1. A Message from #2 2. A History Lesson 3. A Birthday Present

3. Greetings from Yi Wan
“I treasure many fond memories while being your student. I still remember the lovely way you smile.”
“One day a visiting faculty from Tsinghua Univiersity (considered the best engineering school in China) came to my office. After he learned that I was your student, he highly praised you and said that I must be somebody because of your fame .”

4. Backdrop 1995: PhD, UW 1995-96: postdoc, Rice
: Asst. Prof. at MSU
Focus on “classical” signal and image processing (filtering, wavelets, multiscale methods)
1999: Hired by Rice as an Assistant Professor

5. New Millenium, New Horizons (and New Looks)
Network Science
Inverse Problems
Machine Learning

6. Computer Vision?
TEMPLAR: TEMPlate Learning from Atomic Representations

7. Dyadic, Coarse-to-Fine Thinking
Function approximation Set boundary approximation

8. Dyadic, Coarse-to-Fine Thinking
Classification (Clay)
Density estimation (Becca)
Density level sets (Aarti, Clay)
Regression level sets (Becca)
Active learning (Rui, Becca)
Semi-supervised learning (Aarti)

9. Influences David Donoho (wedgelets, CART/best basis)
Andrew Barron (complexity regularization, sieves)
Polonik/Tsybakov (rate conditions)

10. Contributions Emphasis on approximation error
Optimal rates of convergence
Adaptivity

11. Lessons Learned Analysis of estimation error (CORT)
Distributional assumptions
Problems of interest
Limitations of dyadic thinking

12. Q & A More influences, contributions, or lessons learned?
What are the most important legacies of this phase of Rob’s career? (for the field, for us as individuals)
How has your background in SP helped you address ML problems? (or vice versa)
What are the keys to successfully entering a new field?

13. Birthday Present

14. Linear Preference Model

15. Suboptimality of Preference Learning