UT Data Mining Research Group

Spring 2009 Meeting Time

We will meet on Tuesdays 3-4PM. The first meeting will be on Feb 10th, 3-4pm in ACES 2.402.

Scheduled Meetings

Date	Time	Place	Topic
2/10/2009	3PM	ACES 2.402	TBD

* Future meetings subject to rearrangement.
* Please send in suggestions for new papers to discuss, or vote for a paper from the currently proposed papers.

Past Meetings

Date	Time	Place	Topic
11/5/2008	1PM	ACES 2.402	Tutorial on Structured Prediction Suggested readings: I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun Large Margin Methods for Structured and Interdependent Output Variables, JMLR, 6(Sep):1453-1484, 2005 T. Joachims, T. Finley and Chun-Nam Yu Cutting-Plane Training of Structural SVMs, Machine Learning, to appear. B. Taskar, V. Chatalbashev, D. Koller and C. Guestrin Learning Structured Prediction Models: A Large Margin Approach, ICML, 2005 B. Taskar, S. Lacoste-Julien, and M. Jordan Structured Prediction, Dual Extragradient and Bregman Projections, JMLR, Special Topic on Machine Learning and Large Scale Optimization
11/12/2008	1PM	ACES 2.402
9/24/2008	1PM	ACES 2.402	Tutorial on Graphical models and variational methods: Message-passing and relaxations Suggested readings: Martin J. Wainwright and Michael I. Jordan Graphical models, exponential families, and variational inference Sections 1, 2.1, 2.2, 2.3, 2.4.1, 2.5, 2.6, 3 Christopher Bishop Pattern Recognition and Machine Learning Chapter 8
10/1/2008	1PM	ACES 2.402
10/8/2008	1PM	PAI 3.14
09/10/2008	1PM	ACES 6.336	Shai Shalev-Shwartz and Nathan Srebro SVM Optimization: Inverse Dependence on Training Set Size, ICML, 2008

* For older discussions, see Previous Discussions

Members

Faculty

• Raymond J. Mooney
• Inderjit S. Dhillon
• Joydeep Ghosh
• Jason Michael Baldridge

Post-Doc

• Zhengdong Lu
• Rohit Kate

Student

• David Chen
• Lily Mihalkova
• Tuyen Ngoc Huynh
• Sonal Gupta
• Joo Hyun Kim
• Joseph Reisinger
• Prateek Jain
• Wei Tang
• Clinton Jones
• Manish Katyal
• Alex Liu
• Priyank Patel

Previous Members

Proposed Readings

Papers

NIPS 2008

• Measures of Clustering Quality: A Working Set of Axioms for Clustering Shai Ben-David, Margareta Ackerman
• Influence of graph construction on graph-based clustering measures Markus Maier, Ulrike Von Luxburg, Matthias Hein
• Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning Francis Bach
• Syntactic Topic Models Jordan L Boyd-Graber, David Blei
• Covariance Estimation for High Dimensional Data Vectors Using the Sparse Matrix Transform Guangzhi Cao, Charles Bouman
• The Infinite Factorial Hidden Markov Model Jurgen Van Gael, Yee Whye Teh, Zoubin Ghahramani
• Support Vector Machines with a Reject Option Yves Grandvalet, Alain Rakotomamonjy, Joseph Keshet, Stephane Canu
• Fast Prediction on a Tree Mark Herbster, Massimiliano Pontil, Sergio Rojas Galeano
• Clustering via LP-based Stabilities Nikos Komodakis, Nikos Paragios, Georgios Tziritas
• Dimensionality Reduction for Data in Multiple Feature Representations Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh
• Unlabeled data: Now it helps, now it doesn't Aarti Singh, Robert Nowak, Xiaojin Zhu
• Translated Learning: Transfer Learning across Different Feature Spaces Wenyuan Dai, Yuqiang Chen, Gui-Rong Xue, Qiang Yang, Yong Yu

Tutorials

• Compressed Sensing by Emmanuel Candes (nips08)
• Sample Selection Bias Covariate Shift: Problems, Solutions, and Applications by Wei Fan and Masashi Sugiyama (icdm08)
• Detecting Clusters in Moderate-to-High Dimensional Data: Subspace Clustering, Pattern-based Clustering, and Correlation Clustering by Hans-Peter Kriegel, Peer Kroger, and Arthur Zimek (kdd08, pakdd08, icdm07, vldb08)
• Practical Relational Data Community Generation Bo Long, Zhongfei Zhang (icdm07, icml07)

ICML 2008

• Painless embeddings of distributions: the function space view Alex Smola, Arthur Gretton, and Kenji Fukumizu
• Dimensionality Reduction the Probabilistic Way Neil Lawrence
• Graphical models and variational methods: Message-passing and relaxations Martin Wainwright
• Beyond Convexity: Submodularity in Machine Learning Andreas Krause, Carlos Guestrin
• Tutorial on Theory and Applications of Online Learning Shai Shalev-Shwartz, Yoram Singer
• Sparse Linear Models: Bayesian Inference and Experimental Design Matthias Seeger

ICML 2007

• Mining Large Time Evolving Data Using Matrix and Tensor Tools Christos Faloutsos, Tamara G. Kolda and Jimeng Sun
• Semi-Supervised Learning Xiaojin Zhu

NIPS 2007 (with video)

• Learning Using Many Examples Leon Bottou, Andrew Moore
• Deep Belief Nets Geoffrey Hinton
• Structured Prediction Ben Taskar

Nips06

• Energy-Based Models: Structured Learning Beyond Likelihoods Yann LeCun

COMPETITION BASED

• ICML'08 Workshop PASCAL Large Scale Learning Challenge
• Netflix challenge Robert Bell and Yehuda Koren
• KDD Cup 2008 competition The competition focuses on the early detection of breast cancer from X-ray images of the breast.

If you find a certain paper interesting and would like to recommend reading, please feel free to let us know during the meeting or e-mail Prateek Jain.

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