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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NC, MBE (Joint) |
2009-03-12 14:50 |
Tokyo | Tamagawa Univ. | Adaptive Importance Sampling with Automatic Model Selection in Reward Weighted Regression Hirotaka Hachiya (Tokyo Inst. of Tech.), Jan Peters (Max Planck Inst. of Tech.), Masashi Sugiyama (Tokyo Inst. of Tech.)  NC2008-145 |
Direct policy search is a useful framework of reinforcement learning in particular in continuous systems such as robot c... [more] | NC2008-145 pp.249-254 |
| NC, MBE (Joint) |
2009-03-13 10:10 |
Tokyo | Tamagawa Univ. | EEG classification method by adaptive downsampling with training data
-- Experiment with P300 Speller task -- Yuya Sakamoto, Masaki Aono (Toyohashi Univ. of Tech.) NC2008-165 |
To achieve Brain Computer Interface, recording Electroencephalogram (EEG) and classifying whether P300 is evoked by pres... [more] | NC2008-165 pp.365-370 |
| NC, MBE (Joint) |
2008-03-14 13:20 |
Tokyo | Tamagawa Univ | Active sampling based on Gaussian Process for reinforcement learning Kazuhiro Takeda, Takeshi Mori (ＮＡＩＳＴ), Shin Ishii (Kyoto Univ.) NC2007-192 |
In reinforcement learning (RL), many samples are necessary in every policy improvement, which requires the robot actual... [more] |
NC2007-192 pp.473-478 |
| CS, SIP, CAS | 2008-03-06 17:05 |
Yamaguchi | Yamaguchi University | [Invited Talk]
What can we see behind sampling theorems? Hidemitsu Ogawa (Tokyo Univ. Social Welfare) CAS2007-124 SIP2007-199 CS2007-89 |
The problem of sampling theorems is reformulated from the functional analytic point of view. It is shown that the proble... [more] | CAS2007-124 SIP2007-199 CS2007-89 pp.91-96 |
| MBE, NC (Joint) |
2007-12-22 15:45 |
Aichi | Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiyama (Tokyo Inst. of Tech.) NC2007-84 |
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past. A common approach i... [more] | NC2007-84 pp.75-80 |
|
| NC | 2007-06-15 09:00 |
Okinawa | OIST Seaside House | Off-policy least-squares temporal difference learning and its convergence guarantee in finite horizon prorblems Takeshi Mori, Shin-ichi Maeda, Shin Ishii (NAIST) NC2007-14 |
Recently-developed off-policy temporal difference (TD) learning with linear function approximation has attracted attenti... [more] | NC2007-14 pp.35-40 |
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