Papers for Presentation

Here is a list of suggested papers for the paper presentation. You may choose a paper not on the list, but you must consult with the lecturer or the TA over email about it.

The papers labeled below as background reading are considered unsuitable for presentation, but could give you some more insight into the problem domain of the other papers.

Automated Recommender Systems

  1. Roth, Maayan, et al, Suggesting friends using the implicit social graph, in Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (New York, NY, USA: ACM, 2010), 233-242.
  2. Yehuda Koren, Factorization meets the neighborhood: a multifaceted collaborative filtering model, in Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (Las Vegas, Nevada, USA: ACM, 2008), 426-434.
  3. Robert Bell, Yehuda Koren, and Chris Volinsky, Modeling relationships at multiple scales to improve accuracy of large recommender systems, in Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (San Jose, California, USA: ACM, 2007), 95-104. Bell and Koren are two of the authors of the prize-winning Netflix system.
  4. Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey Hinton, Restricted Boltzmann machines for collaborative filtering, in International Conference on Machine Learning (ICML), 2007, 791-798. A bit hard, but this is another one of the key technologies behind the Netflix prize submissions.
  5. Platt et al., Learning a Gaussian Process Prior for Automatically Generating Music Playlists. Advances in Neural Information Processing Systems (NIPS). 2002.
  6. Kai Yu, Anton Schwaighofer, Volker Tresp, Wei-Ying Ma and HongJiang Zhang, Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes in UAI 2003.
  7. Mingqing Hu and Bing Lu., Mining and Summarizing Customer Reviews in KDD 2004

Also, anyone interested in a presentation on Recommender Systems from a historical perspective, could check these:

Document Clustering, Classification and Analysis

  1. Rosen-Zvi, Griffiths, Steyvers, and Smyth., The Author-Topic Model for Authors and Documents.. Conference on Uncertainty in Artificial Intelligence (UAI). 2004. An early and instructive example of how latent Dirichlet allocation can be customized to incorporate extra information.
  2. David M. Blei and John D. Lafferty, Dynamic topic models, in Proceedings of the 23rd International Conference on Machine learning (Pittsburgh, Pennsylvania: ACM, 2006), 113-120.
  3. Dafna Shahaf and Carlos Guestrin., Connecting the Dots Between News Articles. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2010.
  4. Xiaohua Hu et al., Exploiting Wikipedia as external knowledge for document clustering, in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (Paris, France: ACM, 2009), 389-396.
  5. Jun Zhu et al., Simultaneous record detection and attribute labeling in web data extraction, in Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (Philadelphia, PA, USA: ACM, 2006), 494-503.

Making and combining clasifiers

  1. Rajat Raina et al., Self-taught learning: transfer learning from unlabeled data, in Proceedings of the 24th International Conference on Machine learning (Corvalis, Oregon: ACM, 2007), 759-766.
  2. Yoshua Bengio et al., Curriculum learning, in Proceedings of the 26th Annual International Conference on Machine Learning (Montreal, Quebec, Canada: ACM, 2009), 41-48.
  3. Nicolò Cesa-Bianchi, Claudio Gentile, and Luca Zaniboni, Hierarchical classification: combining Bayes with SVM, in Proceedings of the 23rd international conference on Machine learning (Pittsburgh, Pennsylvania: ACM, 2006), 177-184.

Pattern Discovery

  1. Fosca Giannotti et al., Trajectory pattern mining, in Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (San Jose, California, USA: ACM, 2007), 330-339.

Social networks

  1. Bee-Chung et al., User reputation in a comment rating environment, in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (New York, NY, USA: ACM, 2011), 159-167.
  2. Lars Backstrom et al., Group formation in large social networks: membership, growth, and evolution, in Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (San Diego, California, USA 2011), 159-167.
  3. Jure Leskovec et al., Microscopic evolution of social networks, in Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (Las Vegas, Nevada, USA: ACM, 2008), 462-470.
  4. Shen et al. Latent Friend Mining from Blog Data. International Conference on Data Mining. 2006.
  5. Tan, C., Lee, L., Tang, J., Jiang, L., Zhou, M. and Li, P. User-Level Sentiment Analysis Including Social Networks. KDD 2011.

Web mining

  1. El-Arini, Khalid and Guestrin, Carlos, Beyond keyword search: discovering relevant scientific literature in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (New York, NY, USA: 2011), 439-447
  2. Leskovec, Backstrom, and Kleinberg., Meme-tracking and the dynamics of the news cycle, KDD 2009. (A data mining approach to tracking memes in blogs and mainstream media.)
  3. Huanhuan Cao et al., Context-aware query suggestion by mining click-through and session data, in Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (Las Vegas, Nevada, USA: ACM, 2008), 875-883.
  4. Ricardo Baeza-Yates and Alessandro Tiberi, Extracting semantic relations from query logs, in Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (San Jose, California, USA: ACM, 2007), 76-85.
  5. Filip Radlinski, Robert Kleinberg, and Thorsten Joachims, Learning diverse rankings with multi-armed bandits, in Proceedings of the 25th international conference on Machine learning (Helsinki, Finland: ACM, 2008), 784-791.

Background Reading:

Natural Language Processing

  1. Frustratingly Easy Domain Adaptation by Hal Daume. ACL 2007
  2. Shallow Parsing with Conditional Random Fields. Fei Sha and Fernando Pereira. Proceedings of Human Language Technology-NAACL 2003
  3. A Probabilistic Framework for Semi-Supervised Clustering by Sugato Basu, Mikhail Bilenko and Raymond J. Mooney. In KDD 2004.

Web Mining (and other text analysis)

  1. Snowsill, Tristan Mark et. al. Refining causality: who copied from whom? in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (San Diego, California, USA: ACM, 2011), 466-474
  2. D. Sculley, Combined regression and ranking, in Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (New York, NY, USA: ACM, 2010), 979-988.
  3. Tracking evolving communities in large linked networks by John Hopcroft and Omar Khan and Brian Kulis and Bart Selman. In PNAS 101 suppl. 1, 2004.
  4. Justin Ma et al., Beyond blacklists: learning to detect malicious web sites from suspicious URLs, in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (Paris, France: ACM, 2009).

Bioinformatics

  1. A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences by E.P. Xing, M.I. Jordan, R.M. Karp and S. Russell. In Advances in Neural Information Processing Systems 15 ( NIPS2002). A longer version is available from Eric's home page.

Computer Vision

  1. Semi-Supervised Learning in Gigantic Image Collections (2009) Rob Fergus, Yair Weiss, Antonio Torralba
  2. Segmenting Scenes by Matching Image Composites (2009) Bryan Russell, Alyosha Efros, Josef Sivic, Bill Freeman, Andrew Zisserman
  3. Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships (2009) Tomasz Malisiewicz, Alyosha Efros

Image Retrieval by Content, Image Analysis

  1. Blobworld: Image segmentation using Expectation-Maximization and its application to image querying by Chad Carson, Serge Belongie, Hayit Greenspan, and Jitendra Malik. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(8):1026-1038, August 2002.
  2. Object Recognition with Informative Features and Linear Classification by Michel Vidal-Naquet and Shimon Ullman. In ICCV 2003.
  3. Matching Words and Pictures by Kobus Barnard, Pinar Duygulu, Nando de Freitas, David Forsyth, David Blei, and Michael I. Jordan. In Journal of Machine Learning Research, 3, 2003.
  4. Robust Real-Time Face Detection by Paul Viola and Michael J. Jones. In International Journal of Computer Vision 57(2), 2004.

Background Reading:

Other Application of Probabilistic Models

  1. D. Sculley, Combined regression and ranking, in Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (New York, NY, USA: ACM, 2010), 979-988.
  2. Ralf Herbrich, Tom Minka, and Thore Graepel, TrueSkill: A Bayesian Skill Rating System, NIPS 2006.
  3. Localizing Bugs in Program Executions with Graphical Models. Laura Dietz, Valentin Dallmeier, Andreas Zeller, Tobias Scheffer NIPS 2009
  4. Probabilistic analysis of a large-scale urban traffic data set. J. Hutchins, A. Ihler, and P. Smyth. Second International Workshop on Knowledge Discovery from Sensor Data (ACM SIGKDD Conference, KDD-08), August 2008.
  5. HiLighter: Automatically Building Robust Signatures of Performance Behavior for Small- and Large-Scale Systems, Peter Bodik, Moises Goldszmidt, Armando Fox. Third Workshop on Tackling Computer Systems Problems with Machine Learning (SysML '08), San Diego, December 2009
  6. Thore Graepel, Joaquin Quinonero Candela, Thomas Borchert, and Ralf Herbrich. Web-scale Bayesian click-through rate prediction for sponsored search advertising in Microsoft's Bing search engine. ICML 2010
  7. Learning to detect events with Markov-modulated Poisson processes. Alexander Ihler, Jon Hutchins, Padhraic Smyth; ACM Transactions on Knowledge Discovery from Data, Vol 1 Issue 3, Dec. 2007.

Other Applications

  1. Mitchell et al. Learning to Decode Cognitive States from Brain Images. Machine Learning Journal, 2004.
  2. Schmidt, E.M. and Kim, Y.E. Prediction of Time-Varying Musical Mood Distributions Using Kalman Filtering, International Conference on Machine Learning and Applications, 2010

Ideas for Self-Proposed Papers

Inevitably, there are important application areas of machine learning that are not as well represented on this list as they could be. If you are interested in these areas, feel free to propose your own paper. To find a list of papers from a conference, do a Google search like "CVPR 2010" and look for a link that says "Program", "Proceedings", "Accepted papers", or some such. Some ideas are

  1. Computer Vision: Check recent proceedings of CVPR, ICCV
  2. Natural Language Processing: Main conferences include ACL, NAACL, and EMNLP
  3. Bioinformatics
This page was written by Frederick Ducatelle and has been updated and maintained by Charles Sutton, Amos Storkey and Stefanos Angelidis


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