Difference between revisions of "MLReadingGroup"

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We maintain an announcement/discussion list for the reading group. You may sign up for the list [https://lists.cs.princeton.edu/mailman/listinfo/ml-reading/ here].
 
We maintain an announcement/discussion list for the reading group. You may sign up for the list [https://lists.cs.princeton.edu/mailman/listinfo/ml-reading/ here].
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==Schedule (Fall 2008) ==
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Our weekly meetings are '''Mo 1:00-5:00pm''' on the 3rd floor of the CS building (CS 302).
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* Graphical Models, exponential families, and variational inference
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 +
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==Schedule (Spring 2008) ==
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Our weekly meetings are '''Tue 1:00-2:30pm''' in the AI lab on the 4th floor of the CS building (CS 431).
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 +
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Schedule of topics:
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* 13 May 2008
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** Topic: Maximum Entropy Discrimination
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** Leader: Chong Wang
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** Main Paper: [http://people.csail.mit.edu/tommi/papers/JaaMeiJeb-nips99.ps Tommi Jaakkola, Marina Meila, and Tony Jebara, Maximum Entropy Discrimination, In ''NIPS'' 1999.]
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** Long Version: [http://people.csail.mit.edu/tommi/papers/maxent.ps Tommi Jaakkola, Marina Meila, and Tony Jebara, Maximum Entropy Discrimination, Technical Report AITR-1668, MIT, 1999]
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* 6 May 2008
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** Topic: Feature selection for relational data
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** Leader: Jonathan Chang
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** Main Paper: [http://citeseer.ist.psu.edu/635777.html Jensen, Neville, and Hay (2003), Avoiding Bias when Aggregating Relational Data with Degree Disparity]
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** Background: [http://citeseer.ist.psu.edu/jensen02linkage.html Jensen and Neville (2002), Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning]
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* 22 April 2008
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** Topic: Game theory
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** Leader: Indraneel Mukherjee
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** Main Paper: [http://projecteuclid.org/DPubS/Repository/1.0/Disseminate?view=body&id=pdf_1&handle=euclid.pjm/1103044235 An Analog of the Minimax Theorem for Vector Payoffs]
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* 15 April 2008
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** Topic: Conditional Random Fields
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** Leader: Berk Kapicioglu
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** Main Paper: [http://www.seas.upenn.edu/~strctlrn/bib/PDF/crf.pdf J. Lafferty, A. McCallum, and F. Pereira (2001), Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data]
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* 8 April 2008
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** Topic: Online Feature Selection
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** Leader: Melissa Carroll
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** Main Paper: [http://jmlr.csail.mit.edu/papers/volume7/zhou06a/zhou06a.pdf Jing Zhou, Dean P. Foster, Robert A. Stine, and Lyle H. Ungar (2006), Streamwise Feature Selection]
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** Background Paper: [http://www-stat.wharton.upenn.edu/~stine/research/smr.pdf Robert A. Stine (2003), Model Selection using Information Theory and the MDL Principle]
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* 1 April 2008
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** Topic: Reinforcement learning and online learning
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** Leader: Umar Syed
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** Main Paper: [http://books.nips.cc/papers/files/nips20/NIPS2007_0631.pdf Alexander Strehl and Michael Littman (2008), Online Linear Regression and Its Application to Reinforcement Learning]
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** Background Paper: [http://citeseer.ist.psu.edu/638941.html Peter Auer (2002), Using Confidence Bounds for Exploitation-Exploration Trade-offs]
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** Background Paper: [http://citeseer.ist.psu.edu/443693.html Ronen I. Brafman and Moshe Tennenholtz (2002), R-max – A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning]
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* 25 March 2008
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** Topic: Network/Relational Learning
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** Leader: Jonathan Chang 
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** Main Paper: [http://arxiv.org/abs/0803.1628v1 Janne Sinkkonen, Janne Aukia, Samuel Kaski.  Component models for large networks]
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** Background Paper: [http://citeseer.ist.psu.edu/cohn01missing.html D Cohn, T Hofmann.  The Missing Link-A Probabilistic Model of Document Content and Hypertext Connectivity]
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** Background Paper: [http://arxiv.org/abs/0705.4485 Edoardo M Airoldi, David M Blei, Stephen E Fienberg, Eric P Xing.  Mixed membership stochastic blockmodels]
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* 11 March 2008
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** Topic: Online learning with experts
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** Leader: Indraneel Mukherjee
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** Paper: [http://nagoya.uchicago.edu/~jabernethy/Binning.pdf Jacob Abernethy, John Langford, Manfred Warmuth. The Binning Algorithm ]
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* 04 March 2008
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** Topic: Incorporating domain knowledge into POS tagging
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** Leader: Jordan Boyd-Graber
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** Paper: [http://books.nips.cc/papers/files/nips20/NIPS2007_0964.pdf Toutanova, Kristina and Johnson, Mark. A Bayesian LDA-based model for semi-supervised part-of-speech tagging.  (2007)]
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** Paper: [http://portal.acm.org/citation.cfm?id=1219884 Smith, Noah and Eisner, Jason.  Contrastive Estimation: Training Log-Linear Models on Unlabeled Data.  (2005)]
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* 26 February 2008
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** Leader: Berk Kapicioglu
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** Paper: [http://citeseer.ist.psu.edu/celeux95stochastic.html Gilles Celeux, Didier Chauveau, Jean Diebolt. "On Stochastic Versions of the EM Algorithm.", 1995]
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==Schedule (Fall 2007) ==
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Our weekly meetings are '''Wed 4:00-5:30pm''' in the AI lab on the 4th floor of the CS building (CS 431).
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Schedule of topics:
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* 12 December 2007
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** Leader: Zafer Barutcuoglu
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** Paper: [http://www.cs.utoronto.ca/~hinton/absps/fastnc.pdf G.E. Hinton, S. Osindero, Y.W. Teh. "A Fast Learning Algorithm for Deep Belief Nets." Neural Computation, 2006.]
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** More empirical results: [http://www-etud.iro.umontreal.ca/~larocheh/publications/greedy-deep-nets-nips-06.pdf Bengio et al. "Greedy Layer-wise Training of Deep Networks." NIPS, 2006.]
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* 28 November 2007
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** Leader: Umar Syed
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** Paper: [http://www.cs.princeton.edu/~usyed/SyedSchapireNIPS2007.pdf Umar Syed and Robert E. Schapire. "A game-theoretic approach to apprenticeship learning", NIPS (2008)].
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** Background reading: The work is based on [http://www.cs.princeton.edu/~schapire/uncompress-papers.cgi/FreundSc96b.ps Yoav Freund and Robert E. Schapire, "Game theory, on-line prediction, and boosting", COLT (1996)] (see Section 2 and the Appendix).
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* 14 November 2007
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** Leader: Melissa Carroll
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** Paper: [http://www-stat.stanford.edu/~hastie/Papers/B67.2%20(2005)%20301-320%20Zou%20&%20Hastie.pdf Hui Zou and Trevor Hastie. "Regularization and variable selection via the elastic net." (2005) J. R. Statist. Soc. B, 67(2), pp. 301–320.]
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** Background reading: It may be helpful to read up on or review LASSO and LARS.  See [http://www-stat.stanford.edu/~tibs/lasso.html the LASSO Page]
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* 24 October 2007
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** Patrón: Berk Kapicioglu
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** Paper: [http://www.research.microsoft.com/~joshuago/exponentialprior-final.pdf Joshua Goodman. "Exponential Priors for Maximum Entropy Models." North American ACL 2004.]
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Additional topics:
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* relational network models
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* DP + parse trees
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* online learning
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* semi-supervised learning
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* stochastic gradient
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* convex optimizing
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* parallel learning
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* game theory
  
 
==Schedule (Spring 2007) ==
 
==Schedule (Spring 2007) ==
  
Our weekly meetings are '''Thu 1:30-3:00pm''' in the AI lab on the 4th floor of the CS building.
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Our weekly meetings are '''Thu 1:30-3:00pm''' in the AI lab on the 4th floor of the CS building (CS 431).
  
 
Schedule of topics:
 
Schedule of topics:
* '''Feb 2nd''' - Privacy preserving DM (Joe)
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* 29 March 2007
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** Leader: Indraneel Mukherjee
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** Paper: [http://www.cs.berkeley.edu/~feisha/pubs/nips2006.pdf Large Margin Hidden Markov Models for Automatic Speech Recognition]
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* 15 March 2007
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** Leader: Jordan Boyd-Graber
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** Paper 1: [http://www.cs.cornell.edu/home/llee/papers/textstruct.pdf Catching the Drift: Probabilistic Content Models, with Applications to Generation and Summarization (2004)]
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** Paper 2: [http://www.stanford.edu/~mpurver/papers/purver-et-al06acl.pdf Unsupervised topic modelling for multi-party spoken discourse (2006)]
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* 8 March 2007
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** Leader: Umar Syed
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** Paper: [http://ai.stanford.edu/~ang/papers/icml04-apprentice.pdf  Pieter Abbeel and Andrew Y. Ng. "Apprenticeship learning via inverse reinforcement learning." ICML 2004.]
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* 1 March 2007
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** Leader: Miro Dudik
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** Paper: [http://www.cs.princeton.edu/~mdudik/ShalevShwartzSi06.pdf Shai Shalev-Shwartz and Yoram Singer. "Convex Repeated Games and Fenchel Duality."]
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** See also [http://www.cs.huji.ac.il/~shais/papers/ShalevSi06_fench_tech.pdf a more recent version from NIPS 2006]. It contains more references and the math is slightly different; e.g., it introduces strong convexity relative to a norm. I find it a little bit too condensed and more difficult to read.
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* 22 February 2007
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** Leader: Melissa Carroll
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** Paper: [http://www.icml2006.org/icml_documents/camera-ready/055_Hidden_Process_Model.pdf R.A. Hutchinson, T. Mitchell, I. Rustandi. "Hidden Process Models." ICML 2006.]
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** Background on fMRI classification application: [http://www.cs.cmu.edu/afs/cs/project/theo-73/www/papers/mlj04-final-published.pdf T. Mitchell, R. Hutchinson, R. Niculescu, F. Pereira, X. Wang. "Learning to Decode Cognitive States from Brain Images." Machine Learning, 57, 145–175, 2004.]
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* 15 February 2007
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** Leader: Edo Airoldi
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** Paper:  [http://research.microsoft.com/~cmbishop/downloads/Bishop-CVPR-06.pdf J. Lasserre, C. M. Bishop, and T. Minka. "Principled hybrids of generative and discriminative models." CVPR 2006.]
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** Notes:  [ftp://ftp.research.microsoft.com/pub/tr/TR-2005-144.pdf T. Minka. "Discriminative models, not discriminative training." MSR-TR-144 2005.]
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* 8 February 2007
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** Leader: Joe Calandrino
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** Paper:  [http://www.cs.bgu.ac.il/~kobbi/papers/psd.pdf I. Dinur, K. Nissim. "Revealing Information while Preserving Privacy." PODS 2003.]
 
* inverse RL (Umar)
 
* inverse RL (Umar)
 
* disc/gen approaches (Bishop...)
 
* disc/gen approaches (Bishop...)
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* Gaussian processes (Z)
 
* Gaussian processes (Z)
 
* Dirichlet processes
 
* Dirichlet processes
* Semisupervised learning (Florian) (West) (Lafferty, Wasserman)
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* Semisupervised learning (Florian)
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** [http://www.e-publications.org/ims/submission/index.php/STS/user/submissionFile/45?confirm=ab8bceff <b>The use of unlabelled data in predictive modelling</b>, Liang F, Mukherjee S and West M, Statistical Science, to appear.]
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** some paper by Lafferty and Wasserman ?
 
* Quantam neural networks (Vaneet)
 
* Quantam neural networks (Vaneet)
 
* Manifold learning (Z)
 
* Manifold learning (Z)
 
* On-line learning (Berk)
 
* On-line learning (Berk)
 
* Music stuff/transcription (R)
 
* Music stuff/transcription (R)
* Varaitional methods (JC)
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* Variational methods (JC)
 
* Random projections (Charikar)
 
* Random projections (Charikar)
  
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=== Students ===
 
=== Students ===
* Zafer Barutcuoglu, CS
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* Indraneel Mukherjee, CS
 
* Jordan Boyd-Graber, CS
 
* Jordan Boyd-Graber, CS
 
* Joseph Calandrino, CS
 
* Joseph Calandrino, CS
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* Melissa Carroll, CS
 
* Jonathan Chang, EE
 
* Jonathan Chang, EE
 
* Miroslav Dudik, CS
 
* Miroslav Dudik, CS
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* Berk Kapicioglu, CS
 
* Berk Kapicioglu, CS
 
* Umar Syed, CS
 
* Umar Syed, CS
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* Chong Wang, CS
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* Sina Jafarpour, CS
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* Sean Gerrish, CS
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* Richard Socher, CS

Latest revision as of 09:18, 3 February 2009

Machine Learning Reading Group

Welcome to the wiki of the machine learning reading group.

Mailing list

We maintain an announcement/discussion list for the reading group. You may sign up for the list here.

Schedule (Fall 2008)

Our weekly meetings are Mo 1:00-5:00pm on the 3rd floor of the CS building (CS 302).

  • Graphical Models, exponential families, and variational inference


Schedule (Spring 2008)

Our weekly meetings are Tue 1:00-2:30pm in the AI lab on the 4th floor of the CS building (CS 431).


Schedule of topics:

Schedule (Fall 2007)

Our weekly meetings are Wed 4:00-5:30pm in the AI lab on the 4th floor of the CS building (CS 431).

Schedule of topics:

Additional topics:

  • relational network models
  • DP + parse trees
  • online learning
  • semi-supervised learning
  • stochastic gradient
  • convex optimizing
  • parallel learning
  • game theory

Schedule (Spring 2007)

Our weekly meetings are Thu 1:30-3:00pm in the AI lab on the 4th floor of the CS building (CS 431).

Schedule of topics:

Schedule (Fall 2006)

Our weekly meetings are Fridays, 3pm to 5pm, in CS 402.

Scheduled readings:

Proposed Topics and Papers

Please add further topics, suggest papers for particular topics, etc. here.

Participants

(Participants, please add your name to the list below.)

Faculty

  • David Blei
  • Rob Schapire

PostDocs

  • Edo Airoldi, LSI & CS
  • Florian Markowetz, LSI

Students

  • Indraneel Mukherjee, CS
  • Jordan Boyd-Graber, CS
  • Joseph Calandrino, CS
  • Melissa Carroll, CS
  • Jonathan Chang, EE
  • Miroslav Dudik, CS
  • Rebecca Fiebrink, CS
  • Berk Kapicioglu, CS
  • Umar Syed, CS
  • Chong Wang, CS
  • Sina Jafarpour, CS
  • Sean Gerrish, CS
  • Richard Socher, CS