Advice for Graduate Students in Statistics

Advice for Graduate Students in Statistics by Michael Steel. I like this guy! (:

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Nonparametric Bayesian Methods

I collect a few references/tutorials to nonparametric Bayesian methods for inference.

I will try to collect a few papers that analyze the convergence rate of these nonparametric Bayesian methods in a future post. I remember I’d found a few! (;

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Reinforcement Learning blog

Satinder Singh started a new blog named Reinforcement Learning blog. Now, I can see that Michael Littman is one of the authors of the blog – though he hasn’t published any post yet. This is a good news for RL. Good luck to them!

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Bracketing Entropy Bounds for Distribution Function

Sh. Song and J. Wellner, “How many distribution functions are there? Bracketing entropy bounds for high-dimensional distribution functions,” 2008.

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Embedding, Metric Entropy, etc.

Dorothee D. Haroske, “Embeddings Of Some Weighted Function Spaces On R_n: Entropy and Approximation Numbers (A Survey of some recent results),” 1997.

Ridgway Scott, “Tutorial on Sobolev Spaces,

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Shannon Sampling and Learning Theory

These two papers by Steve Smale and Ding-Xuan Zhou:

Shannon Sampling and Function Reconstruction from Point Values
Shannon Sampling II. Connections to Learning Theory

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Statistical Performance of Support Vector Machines

Tonight, I found this paper by Blanchard, Bousquet, and Massart: “Statistical Performance of Support Vector Machines” (2008)

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Compression-related ideas in Machine Learning

Shuheng Zhou, John Lafferty, and Larry Wasserman, “Compressed Regression,” 2008.

Boris Ryabko, “Compression-based methods for nonparametric density estimation, online prediction, regression and classification for time series,”

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A few papers on estimation and control of robotic systems

These are papers that I want to read (or can be considered for our reading group).

Seems to be relevant to our IROS 2007 paper (A. M. Farahmand, A. Shademan, and M. Jagersand, “Global Visual-Motor Estimation for Uncalibrated Visual Servoing,” IROS 2007. Check later.

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Tumblr and I

I have used Tumblr microblogging service to organize my machine learning-related stuff for a while. You can see it here.
Although I really like the ease of tumbling, there is a big problem here: it doesn’t naturally come with searching tool and commenting system.
It happened to me that I tried to find some papers that I found a few weeks ago, but I couldn’t find it without going through all the archive. So, I think I need to come back to this blog again.
The problem is that I may just post a link to a paper without any discussion. This may distract you readers. What do you think?! Will you become unhappy if I do so?!

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