Spring 2019 meetings Wednesday 5:00-6:30 approximately every other week

C-301 Conference room Padelford

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Yen-chi Chen and Marina Meila

We will read and discuss foundational papers and themes of Geometric Data Analysis such as

More information and a web page to come soon. If you are interested in participating, please email the organizers. We will aim to meet approximately every other week, i.e. to have 4-5 meetings this quarter.

For student participants: You will not be required to make a presentation/lead a discussion of a paper this quarter, but if you plan to volunteer for one, you can sign up for 1 stat 600 credit with one of the organizers.

List of suggested papers for Spring 2019


Schedule for Spring 2019

[4/10] Sam Koelle on the manifold of shapes. A book and a seminal statistics paper by Le and Kendall

[4/24] Hanyu Zhang Diffusion maps https://www.sciencedirect.com/science/article/pii/S1063520306000546 and https://arxiv.org/abs/0811.0121

[5/8] Zhenman Yuan Spectral clustering

[5/29] Sam Koelle General Exam 1:30 PM PDL C301

[6/5] Daniel Ting -- tentative


Schedule for Winter 2019

[1/30] Gang Cheng on How to tell when a clustering is approximately correct...

[2/6] Yen-Chi Chen Statistical inference with local optima

[2/20] Malcolm Wolff and Hanyu Zhang Kernel density estimation with Locality Sensitive Hashing (Part II)

[3/6] Yikun Zhang 2 step EM for Gaussian mixtures http://cseweb.ucsd.edu/~dasgupta/papers/em.pdf


Schedule for Winter 2018

[1/11] Sam Koelle will present Metric manifold learning: preserving the intrinsic geometry (slides)

[1/25] Yu-Chia Chen will present Improved Graph Laplacian via geometric self-consistency

[2/1]

[2/22]

[3/1]


Schedule for Autumn 2017

[11//30] Daniel Ting (Tableau) on understaning Laplacians (tentative title!)

[11/16] Kitty Mohammed Manifold Learning with KDE and Local PCA

[11/2] Sheridan Grant The nuts and bolts of persistent homology

[10/19] Marina Meila Algorithmics of Manifold Learning

Skim through these before the meeting

Other resources

[10/5] Yen-chi Chen Introduction to TDA after this paper