Search Results for author: Hiroshi Nakagawa

Found 14 papers, 2 papers with code

Differential Privacy without Sensitivity

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.

Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm

no code implementations5 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.

Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring

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).

Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem

1 code implementation8 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.

Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays

1 code implementation2 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.

Quantum Annealing for Variational Bayes Inference

no code implementations9 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.

Quantum Annealing for Dirichlet Process Mixture Models with Applications to Network Clustering

no code implementations19 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).

Stochastic Optimization

Deterministic Single-Pass Algorithm for LDA

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.

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