Thesilog

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

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

• Zoubin Ghahramani, Nonparametic Bayesian Methods (UAI05 slides)
• David Dunson, “Nonparametric Bayes Applications to Biostatistics“.
• Peter Müller and Fernando A. Quintana, “Nonparametic Bayesian Data Analysis,” Statistical Science, 2004.

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! (;

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!

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

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,“

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

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

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,”

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

• Dj. Mitrovic, S. Klanke, and S. Vijayakumar, “Adaptive Optimal Control for Redundancy Actuated Arms,” SAB 2008.
• G. Chesi and Y. S. Hung, “Global Path-Planning for Constrained and Optimal Visual Servoing,” IEEE Trans. Robotics, 2007.

• J.A. Ting, M. Mistry, J. Peters, S. Schaal, J. Nakanishi, “A Bayesian Approach to Nonlinear Parameter Identification for Rigid Body Dynamics,” RSS 2006.

• J.A. Ting, A. D’Souza, S. Vijayakumar, and S. Schaal, “A Bayesian Approach to Empirical Local Linearization for Robotics,” ICRA 2008.

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.

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?!