no code implementations • NeurIPS 2010 • Issei Sato, Kenichi Kurihara, Hiroshi Nakagawa
We develop a deterministic single-pass algorithm for latent Dirichlet allocation (LDA) in order to process received documents one at a time and then discard them in an excess text stream.
no code implementations • 19 May 2013 • Issei Sato, Shu Tanaka, Kenichi Kurihara, Seiji Miyashita, Hiroshi Nakagawa
We developed a new quantum annealing (QA) algorithm for Dirichlet process mixture (DPM) models based on the Chinese restaurant process (CRP).
no code implementations • 9 Aug 2014 • Issei Sato, Kenichi Kurihara, Shu Tanaka, Hiroshi Nakagawa, Seiji Miyashita
This paper presents studies on a deterministic annealing algorithm based on quantum annealing for variational Bayes (QAVB) inference, which can be seen as an extension of the simulated annealing for variational Bayes (SAVB) inference.
1 code implementation • 2 Jun 2015 • Junpei Komiyama, Junya Honda, Hiroshi Nakagawa
Recently, Thompson sampling (TS), a randomized algorithm with a Bayesian spirit, has attracted much attention for its empirically excellent performance, and it is revealed to have an optimal regret bound in the standard single-play MAB problem.
1 code implementation • 8 Jun 2015 • Junpei Komiyama, Junya Honda, Hisashi Kashima, Hiroshi Nakagawa
We study the $K$-armed dueling bandit problem, a variation of the standard stochastic bandit problem where the feedback is limited to relative comparisons of a pair of arms.
no code implementations • NeurIPS 2015 • Junpei Komiyama, Junya Honda, Hiroshi Nakagawa
To show the optimality of PM-DMED with respect to the regret bound, we slightly modify the algorithm by introducing a hinge function (PM-DMED-Hinge).
no code implementations • 5 May 2016 • Junpei Komiyama, Junya Honda, Hiroshi Nakagawa
We study the K-armed dueling bandit problem, a variation of the standard stochastic bandit problem where the feedback is limited to relative comparisons of a pair of arms.
no code implementations • NeurIPS 2016 • Kentaro Minami, Hitomi Arai, Issei Sato, Hiroshi Nakagawa
The exponential mechanism is a general method to construct a randomized estimator that satisfies $(\varepsilon, 0)$-differential privacy.