MLBio+Laboratory Machine Learning in Biomedical Informatics



Data Mining Reading Group

04 Mar 2010
Posted by rangwala

What?: Data Mining Reading Group is a bunch of data mining enthusiasts where we will present a paper to the rest of us every week. Each one of us should try to read this paper critically. The presenter should spend more time thinking about how he/she would like to present the material.

Why?: Several Reasons

- Learn about the state-of-the-art research papers
- Improve on reading papers.
- Improve on presentation skills.

When ? Every Fridays at 11:00 - 12:00 pm.

When ? Large CS Conference Room/Watch the Email

Please edit this table and enter a date when you can present a paper.

Faculty Members (Alphabetical Order):

  • Daniel Barbara
  • Carlotta Domeniconi
  • Jessica Lin
  • Jana Kosecka
  • Huzefa Rangwala

Theme for the Semester: Structured Output Learning/Mutli-Task/Multi-instance learning

Date Presenter Paper
09.16.2011: Sam Blasiak MEDLDA by Zhu et. al.
09.30.2011: Jessica Lin Mining Massive Time Series Database.
10.14.2011 (11-12): Guoxian Yu Semi-Supervised Learning with High Dimensional Data
10.14.2011: Anveshi C

Past DMRG's

09.10.2010 Sam B Latent Dirichlet Allocation by David Blei, Andrew Ng and Michael Jordan Sam will be presenting a detailed derivation of the LDA variational algorithm
09.30.2010 [THU] #4201/11-12 noon Pu W Introduction to Monte Carlo Methods by Mackay
10.08.2010 [FRI] #4201 Muzzamil S Characterizing Microblogs with Topic Models by Ramage et. al.
10.25.2010 Syed F K-Means on Commodity GPUs with CUDA by Hong-Tao et. al.
10.29.2010 Anveshi C Leveraging Sequence Classi cation by Taxonomy-based Multitask Learning
by Widmer et. al
11.05.2010 Tanwishta S A kernel method for unsupervised structured network inference by Lippert et. al
11.12.2010 Zeehasham R Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks by Huang et. al
11.19.2010 Chaitanya Y
03.26.2010, Room #4801 Anveshi C Classification of protein sequences by means of irredundant patterns by Comin et. al.
04.02.2010 Sam B Conditional random fields: Probabilistic models for segmenting and labeling sequence data by Lafferty
04.09.2010 Zeehasham R Using Highly Expressive Contrast Patterns for Classification - Is It Worthwhile ?
04.16.2010 Tanwishta S
04.23.2010 Pu W
04.30.2010 Sheng Li

Potential Papers that can be presented (Please feel free to pick one you had like not on this list):

  1. J. Chang and D. Blei. Relational Topic Models for Document Networks . Artificial Intelligence and Statistics, 2009.
  2. Large Margin Semi-supervised Learning by J Wang
  3. Conditional random fields: Probabilistic models for segmenting and labeling sequence data by Lafferty
  4. An ensemble framework for clustering protein–protein interaction networks by Asur et. al
  5. Kernel methods for predicting protein–protein interactions by Ben-Hur et. al
  6. Using Highly Expressive Contrast Patterns for Classification - Is It Worthwhile ? Elsa Loekito and James Bailey. Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-09),
  7. Mining minimal distinguishing subsequence patterns with gap constraints
    by Xiaonan Ji, James Bailey & Guozhu Dong
  8. Fast subtree kernels on graphs by N. Shervashidze, K. Borgwardt


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