Kazuho Watanabe


Please call me Kazuho. Affiliation : Dept.of Computational Intelligence and Systems Science, Tokyo Institute of Technology
добро пожаловать. Lab : Sumio Watanabe Lab in P&I Lab., Tokyo Institute of Technology.
体重が増えてきました Research : learning theory

CV , Publication List


I received a Ph.D degree from Tokyo-Tech(Sep,29,2006).
Doctoral Dissertation: Statistical Learning Theory of Variational Bayes <ps, pdf>.
This dissertation analyses the variational Bayesian learning algorithms for mixture models.
The approximation accuracy of the variational Bayesian approach is shown by evaluating
the variational stochastic complexity, also known as variational free energy.
Also the main results show how the hyperparameters of the prior distribution influence
the learning process and have some implications for how to design the learning algorithm.

Our paper:"Stochastic complexity for mixture of exponential families in generalized variational Bayes"
will appear in Theoretical Computer Science.

I made a presentation in IDEAL2006.

Our paper:"Stochastic Complexities of General Mixture Models in Variational Bayesian Learning"
has been accepted for publication in Neural Networks, <pdf>.

It's my great pleasure to announce publication of our paper:
"Stochastic Complexities of Gaussian Mixtures in Variational Bayesian Approximation"
Kazuho Watanabe, Sumio Watanabe
JMLR 7(Apr):625--644, 2006 <pdf>.

Recent Activities

Our paper:"Variational Bayesian Stochastic Complexity of Mixture Models" has been accepted for a poster presentation at

An oral presentation at ICONIP2005(Taipei, Taiwan, Oct.30-Nov.2). Paper title:"Variational Bayesian Algorithm and Stochastic Complexity for Mixture Models".

An oral presentation at NOLTA2005(Bruges, Belgium, Oct.18-21). Paper title:"On Variational Bayes Algorithms for Exponential Family Mixtures".

Our paper:"Stochastic complexity for mixture of exponential families in variational bayes" has been accepted for presentation at ALT2005(Singapore,Oct.8-11). In this presentation, I showed the asymptotic behavior of the stochastic complexity (the free energy) in the Variational Bayesian learning of mixture of exponential familiy distributions.

The 3rd Mathematical Science Forum in Tokyo Tech. I made a presentation entitled "Stochastic complexity and variational approximation".

Poster presentation at 2005 IEEE Tokyo Student Workshop(2005.02.15).

Oral presentation at IEEE International Conference on Cybernetics and Intelligent Systems(2004.12.01).
Proceeding manuscript<ps, pdf>:"Lower bounds of stochastic complexities in variational bayes learning of gaussian mixture models," Proc. of IEEE CIS04,Singapore, pp.99-104, 2004.

Our paper "Estimation of the Data Region Using Extreme-value Distributions" was accepted for the 15th international conference on Algorithmic Learning Theory (ALT2004)(Padova,Italy,Oct.2-5).
I attended the international symposium ISITA2004(Parma,Italy,Oct.10-13) and made a presentation there.

IBIS2003(Kyoto,2003.11.11-12): "Estimating the Data Region Using the Asymptotic Distributions of Extreme-value Statistics"<ps, pdf>

IEICE Neurocomputing Technical meeting at Tokyo Institute of Technology.:"Learning the Data Region using Extreme-value Statistics"<ps, pdf>

"Upper Bounds of Bayesian Generalization Errors in Reduced Rank Regression"IEICE Trans., Vol.J86-A,No.3,pp.278-287,2003

The English abstract of this paper is here.


"Learning Curves of Reduced Rank Regression in Bayesian Estimation"<ps, pdf>

Attending the meeting of IEICE Neurocomputing Technical Group at Tamagawa Univ.(2002.3.18)

"Bayes Generalization Errors of Reduced Rank Approximation"<ps,pdf>.

power point:about learning theory and its application(in Japanese)

seminar notes(ps,pdf ):cited from "Learning Machines and Algorithms"(Kyoritu Shuppan)p.42〜p.46 written by Sumio Watanabe

e-mail write to me please!!