The room for CSE 254 is HSS 2321. The class meets on Tuesdays and Thursdays from 3:30pm to 4:50pm, beginning on April 3. Because several students have conflicts, we will not change the time or room. The last meeting will be on June 8.
This year CSE 254 has a specific topic: conditional random fields, known as CRFs. See here for tutorials and papers on CRFs, and see here for available software. We will start by covering loglinear methods, which are the foundation of CRFs. We may also cover related research on other methods for solving structured prediction problems, and/or on topic models.
For the first few weeks, the instructor will lecture on loglinear methods, topic models, and CRFs. There are two assignments Then, in each class meeting, one student will give a talk lasting about 60 minutes presenting a recent technical paper in detail. In questions during the talk, and in the final 20 minutes, all seminar participants will discuss the paper and the issues raised by it.
date  topic or presenter (not confirmed) 

April 3  overview, loglinear models  
April 5 
No class: Rockwood lecture at 4pm by Tomaso Poggio 

April 10 
loglinear models continued  Assignment 1: naive Bayes vs loglinear 
April 12  nonlinear optimization; CRFs 

April 17  CRFs continued  Assignment 2: CRFs 
April 19  Sanjoy Dasgupta 
Maximum entropy and all that: information geometry 
April 24  Eric Wiewiora 
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data by J Lafferty, A McCallum, F Pereira, ICML 2001. 
April 26  Peter Shin  Michael Collins. Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms. Proceedings of the ACL02 conference on Empirical methods in natural language processing, pp.18, 2002. 
May 1  Guilherme Hoefel 
Ben Taskar, Carlos Guestrin and Daphne Koller. MaxMargin
Markov Networks. In Advances in Neural Information Processing
Systems 16 (NIPS 2003), 2004. 
May 3  lecture 

May 8  Panagiotis Voulgaris  Andrew McCallum. Efficiently Inducing Features of Conditional Random Fields. In Proceedings of the 19th Conference in Uncertainty in Articifical Intelligence (UAI2003), 2003. 
May 10  Tom Griffiths  Mandler Hall, room 3545, 4pm: "Everyday Inductive Leaps: Making Predictions and Detecting Coincidences" 
May 15  Pouya Bozorgmehr  Xuming He, Richard Zemel, and Miguel Á. CarreiraPerpiñán. Multiscale conditional random fields for image labelling. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2004. 
May 17  Simone McCloskey  Multilabelled classification using maximum entropy method by S Zhu, X Ji, W Xu, Y Gong, SIGIR 2005. 
May 22  Tingfan Wu  Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, Yasemin Altun. Large Margin Methods for Structured and Interdependent Output Variables. JMLR, December 2005. 
May 24  Nicholas Butko 
Sham Kakade, Yee Whye Teh, Sam T. Roweis. An Alternate Objective Function for Markovian Fields. ICML 2002. 
May 29  Alexander Loukissas  Samuel Gross, Olga Russakovsky, Chuong Do, and Serafim Batzoglou. Training conditional random fields for maximum labelwise accuracy. In Advances in Neural Processing Systems 19 (NIPS), December 2006. 
May 31  
June 5  
June 7  
finals week  project presentations 
The instructor is Charles Elkan (Professor), whose office is in the CSE building, room 4134. Feel free to send email to arrange an appointment, or telephone (858) 5348897.
Note: The seminar will run in parallel with a data mining contest sponsored by Fair Isaac, with cash
prizes.
Each presentation should be prepared using LaTeX or Powerpoint, and should consist of about 40 slides. You must copy all important equations, diagrams, charts, and tables from the paper into your slides.
For each paper, we will have a webbased discussion area. Each student is expected to contribute at least one message to the discussion, before the presentation. A message may ask an interesting question, point out a strength or weakness of the paper, or answer a question asked by someone else. Messages should be thoughtful!
The schedule of presentations will be determined as early as possible. Students should choose a date first, and then agree with the instructor about a paper to present. To find ideas, students can look at this list of possible papers and contact the instructor.
If you want to change your presentation date, please arrange a swap
with another student and notify the instructor at least two weeks in
advance.
Most recently updated on May 14, 2007 by Charles Elkan, elkan@cs.ucsd.edu