Data Mining Reading Group
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):
- J. Chang and D. Blei. Relational Topic Models for Document Networks . Artificial Intelligence and Statistics, 2009.
- Large Margin Semi-supervised Learning by J Wang
- Conditional random fields: Probabilistic models for segmenting and labeling sequence data by Lafferty
- An ensemble framework for clustering protein–protein interaction networks by Asur et. al
- Kernel methods for predicting protein–protein interactions by Ben-Hur et. al
- 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),
- Mining minimal distinguishing subsequence patterns with gap constraints
by Xiaonan Ji, James Bailey & Guozhu Dong - Fast subtree kernels on graphs by N. Shervashidze, K. Borgwardt
