Data Mining Seminar : Sampling
Instructor : Jeff Phillips
Spring 2012 | Wednesday 1:25 pm - 2:45 pm
Location : MEB 3147 (the LCR)
Catalog number: CS 7931 01

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Description:
One of the most obvious ways to deal with the "big data" problem is to simply sample the "big" data set to create a smaller one. Then we can run our algorithms/analysis on the smaller data set, which will complete much more quickly. This raises three obvious questions:

• 1: how much and what kinds of accuracy can be obtain from these samples?
• 2: what is the most efficient/effective way to draw these samples?
• 3: can we do better than blind random samples?

This seminar will answer these questions, covering classic results as well as fascinating recent approaches.

This 1-credit seminar will meet once a week and be student driven; each student will be responsible for giving 1 lecture in class (or less depending on class size). Topics will include:

• learning theory results on accuracy of samples
• Markov chain sampling (from probe-only distributions), including Gibbs sampling and other state-of-the-art variants
• efficient ways to sample from massive data
• non-uniform approaches including discrepancy-based and others

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Schedule:

Date	Topic	Speaker
Wed 8.22	Overview (JEFF-NOTES)	Jeff Phillips
Wed 8.29	--- Travel Day ---
Wed 9.05	Accuracy: Properties (e.g. eps-samples and eps-nets, and more details on proof) (JEFF-NOTES)	Jeff Phillips
Wed 9.12	Accuracy: Importance ( WP ) and Rejection ( WP ) Sampling ( notes )	Poonam Ekhelikar
Wed 9.19	Markov Chains: Definitions and Introduction ( WP, Chapter 1 )	Yan Zheng / Chancey Ding
Wed 9.26	Markov Chains: Rapidly Mixing and Convergence Analysis ( Conductance opt2 opt3 | Chapter 4 )	Haibo Ding / Chad Miller
Wed 10.03	Markov Chains: Metropolis ( WP, for ML, Chapter 10 ) and Gibbs ( WP, Chapter 6 ) Sampling ( notes, BUGS )	Parasaran Raman / Nazmus Saquib
Wed 10.10	(Fall Break - No Class)
Wed 10.17	Markov Chains: Advanced Sampling ( WP, tempered analysis )	John Moeller
Wed 10.24	Markov Chains: Coupling from the Past ( WP, Java App, Chapter 22, more )	Prasana Muralidharan
Wed 10.31	Efficiency: Reservoir ( WP, Vitter, blog ) and Distributed ( distributed, frequency ) Sampling	Gholamreza Esfandani / Mina Ghashami
Wed 11.07	Efficiency: Variance-Optimal Sampling ( Priority Sampling, VarOpt Sampling )	Miaomiao Zhang / Xing Lin
Wed 11.14	Efficiency: L_p Sampling ( L_p | Precision sampling | improved | McGregor slides )	Suresh Venkatasubramanian
Wed 11.21	Beyond Random: Discrepancy and How to Use it ( WP, Chazelle, Matousek )	Supraja Jayakumar / Abishek Trivedi
Wed 11.28	Beyond Random: Hyperbolic Cosine ( Chazelle, ) + other discrepancy-coloring methods ( edge walking )	Samira Daruki / Prafulla Surve
Wed 12.05	Beyond Random: Structure-aware VarOpt Sampling ( range, stream )	Amir Abdullah

Description:
Most resources will be linked directly from each weeks row in the above schedule. However, some great references are available on the more general topics of Markov Chains.

A First Course in Bayesian Statistical Analysis by Peter Hoff.
Provides a nice view of practical MCMC sampling for Bayesian statistics.

Markov Chains and Mixing Times by David A. Levin and Yuval Peres and Elizabeth L. Wilmer.
Provides a nice overview of the theory behind Markov Chains.