One of the course requirements is to do a project or presentation. Here is some more information on what is expected, and some ideas to get you started.

**Presentations:**- These should be done in groups of 2. You will read one or more papers and produce two things: a class presentation, and a 5-page class handout on the topic. You can have one person do the presentation and one person do the handout, or split both 50/50. The topic should be chosen in consultation with the instructor.
**Projects:**- These can be done individually or in groups of 2. A project might involve conducting an experiment or thinking about a theoretical problem, or trying to relate two problems. The end result should be a 5-10 page report, and if you wish, a 5-10 minute presentation.

- Algorithms for learning DNF formulas in various models.
- J. Jackson. An Efficient Membership-Query Algorithm for Learning DNF with Respect to the Uniform Distribution. Journal of
Computer and System Sciences 55(3), 1997.
Or, more recently:
N. Bshouty, J. Jackson, and T. Tamon. More Efficient PAC-learning of
DNF with Membership Queries Under the Uniform
Distribution. Proceedings of the 12th Annual Workshop on Computational
Learning Theory, 1999.

These papers show how to use membership queries to learn DNF formulas respect to the uniform distribution. Combination of fourier analysis with boosting and a clever analysis of the distributions produced. - N. Bshouty and E. Mossel and R. O'Donnell and R. Servedio.
Learning DNF from Random Walks. 44th Annual Symposium on Foundations
of Computer Science (FOCS), 2003, pp. 189-198.

Improves on above results by replacing membership queries with passive observation of a random walk on {0,1}^n. That is, you get to observe a sequence of examples where each new example is a random neighbor of the previous one.

- J. Jackson. An Efficient Membership-Query Algorithm for Learning DNF with Respect to the Uniform Distribution. Journal of
Computer and System Sciences 55(3), 1997.
Or, more recently:
N. Bshouty, J. Jackson, and T. Tamon. More Efficient PAC-learning of
DNF with Membership Queries Under the Uniform
Distribution. Proceedings of the 12th Annual Workshop on Computational
Learning Theory, 1999.
- Learning functions of
*r*relevant variables. See E. Mossel and R. O'Donnell and R. Servedio. Learning Juntas. 35th Annual Symposium on Theory of Computing (STOC), 2003, pp. 206-212. - Online learning algorithms:
- A. Kalai and S. Vempala.
Efficient
algorithms for online decision problems, COLT 2003.

This paper gives a simple algorithm for the experts problem that generalizes to a broad class of settings such as online shortest paths (each day you need to drive to work, but traffic patterns are totally different; your goal is to do nearly as well as the best fixed path in hindsight. The issue here is that a graph G might have exponentially many paths so you don't want to represent each path explicitly as a different "expert"). Basically, they show the following simple approach works: choose the path that did best in hindsight, but where you "hallucinate" an additional day 0 with a random traffic pattern. - Extension of experts setting to specialists/sleeping-experts. Extensions to "internal regret". Extension to "bandit" setting. "Apple-tasting" model. (Ask me for papers).
- On-line learning and Bregman divergences. The top couple entries
on this page
are some tutorials by Manfred Warmuth.

- A. Kalai and S. Vempala.
Efficient
algorithms for online decision problems, COLT 2003.
- Boosting: (See Rob Schapire's
page and especially this
overview for more)
- Robert E. Schapire and Yoram Singer.
Improved boosting algorithms using confidence-rated predictions.
Machine Learning, 37(3):297-336, 1999.

A boosting algorithm for weak-hypotheses that include confidences in their predictions. E.g., can think of it like a smooth version of decision lists. - Robert E. Schapire, Yoav Freund, Peter Bartlett and Wee Sun Lee.
Boosting the margin: A new explanation for the effectiveness of voting methods
The Annals of Statistics, 26(5):1651-1686, 1998.

A margin-based analysis of generalization error of boosting.

- Robert E. Schapire and Yoram Singer.
Improved boosting algorithms using confidence-rated predictions.
Machine Learning, 37(3):297-336, 1999.
- Learning in Markov Decision Processes. See Michael Kearns's home
page and Yishay Mansour's home page for a number of good papers.
- Support vector machines, kernel-based algorithms.
Margins and luckiness functions. Random projection
viewpoint.
- PAC-Bayes bounds, shell-bounds, other methods of obtaining
confidence bounds.
- Papers on clustering
- Learning in Graphical Models (Bayes Nets)

- You might do a project based on one of the above papers/topics or
a paper/topic discussed in class. E.g., This could involve
implementing an algorithm and trying it out on a learning problem,
playing with some of the design issues involved. Or you could try
improving/simplifying some of the theoretical results. Or
extending/applying them to a different setting.
- You could think of a new learning problem or theoretical model
that gets at some issue not addressed by the standard problems and models
discussed in class. See if you can analyze the model or solve the
problem.
- Try your hand at one of the open problems discussed in class.
- Or, suggest something...

Avrim Blum Last modified: Tue Mar 02 09:14:40 Eastern Standard Time 2004