Difference between revisions of "Class meeting for 10-605 LDA"

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This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2014|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Spring_2014]].
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This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2016|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2016]].
  
 
=== Slides ===
 
=== Slides ===
  
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* Lecture 1: [http://www.cs.cmu.edu/~wcohen/10-605/lda-1.pptx Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-605/lda-1.pdf PDF].
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* Lecture 2: [http://www.cs.cmu.edu/~wcohen/10-605/lda-2.pptx Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-605/lda-2.pdf PDF].
  
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* [http://www.cs.cmu.edu/~wcohen/10-605/topic-models-intro.pptx LDA and Block Models]
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=== Quiz ===
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* [http://www.cs.cmu.edu/~wcohen/10-605/fastlda.pptx Scaling up LDA]
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* No quiz for lecture 1
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* [https://qna.cs.cmu.edu/#/pages/view/105 Quiz for lecture 2]
  
 
=== Readings ===
 
=== Readings ===
  
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Basic LDA:
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* Blei, David M., Andrew Y. Ng, and Michael I. Jordan. "Latent Dirichlet allocation." Journal of machine Learning research 3.Jan (2003): 993-1022.
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Speedups for LDA:
  
 
* [http://jmlr.csail.mit.edu/papers/volume10/newman09a/newman09a.pdf Distributed Algorithms for Topic Models], Newman et al, JMLR 2009.
 
* [http://jmlr.csail.mit.edu/papers/volume10/newman09a/newman09a.pdf Distributed Algorithms for Topic Models], Newman et al, JMLR 2009.
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* [http://www.ics.uci.edu/~newman/pubs/fastlda.pdf Fast Collapsed Gibbs Sampling for LDA], Porteous et al, ...
 
 
* [http://people.cs.umass.edu/~mimno/papers/fast-topic-model.pdf Efficient Methods for Topic Model Inference on Streaming Document Collections], Yao, Mimno, McCallum KDD 2009.
 
* [http://people.cs.umass.edu/~mimno/papers/fast-topic-model.pdf Efficient Methods for Topic Model Inference on Streaming Document Collections], Yao, Mimno, McCallum KDD 2009.
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* [http://dl.acm.org/citation.cfm?id=2623756 Reducing the sampling complexity of topic models], Li, Ahmed, Ravi, & Smola, KDD 2014
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* [https://dl.acm.org/citation.cfm?id=2741682 A Scalable Asynchronous Distributed Algorithm for Topic Modeling], Yu, Hsieh, Yun, Vishwanathan, Dillon, WWW 2015
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=== Things to remember ===
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* How Gibbs sampling is used to sample from a model.
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* The "generative story" associated with key models like LDA, naive Bayes, and stochastic block models.
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* What a "mixed membership" generative model is.
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* The time complexity and storage requirements of Gibbs sampling for LDAs.
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* How LDA learning can be sped up using IPM approaches.
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* Why efficient sampling is important for LDAs
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* How sampling can be sped up for many topics by preprocessing the parameters of the distribution
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* How the storage used for LDA can be reduced by exploiting the fact that many words are rare.

Latest revision as of 10:31, 20 November 2017

This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall_2016.

Slides

Quiz

Readings

Basic LDA:

  • Blei, David M., Andrew Y. Ng, and Michael I. Jordan. "Latent Dirichlet allocation." Journal of machine Learning research 3.Jan (2003): 993-1022.

Speedups for LDA:

Things to remember

  • How Gibbs sampling is used to sample from a model.
  • The "generative story" associated with key models like LDA, naive Bayes, and stochastic block models.
  • What a "mixed membership" generative model is.
  • The time complexity and storage requirements of Gibbs sampling for LDAs.
  • How LDA learning can be sped up using IPM approaches.
  • Why efficient sampling is important for LDAs
  • How sampling can be sped up for many topics by preprocessing the parameters of the distribution
  • How the storage used for LDA can be reduced by exploiting the fact that many words are rare.
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