`Travelled to:`

1 × Switzerland

1 × United Kingdom

2 × Australia

2 × China

3 × USA

`Collaborated with:`

H.Nakagawa B.Yang M.Yoshida H.Kashima K.Kurihara J.Sakuma M.Ikeda S.Ono

`Talks about:`

use (4) dirichlet (3) process (3) collaps (3) variat (3) model (3) infer (3) bay (3) discoveri (2) stochast (2)

## Person: Issei Sato

### DBLP: Sato:Issei

### Contributed to:

### Wrote 11 papers:

- KDD-2015-SatoN #online #probability
- Stochastic Divergence Minimization for Online Collapsed Variational Bayes Zero Inference of Latent Dirichlet Allocation (IS, HN), pp. 1035–1044.
- SIGMOD-2015-YangSN #correlation #difference #privacy
- Bayesian Differential Privacy on Correlated Data (BY, IS, HN), pp. 747–762.
- ICML-c2-2014-SatoKN #analysis #normalisation
- Latent Confusion Analysis by Normalized Gamma Construction (IS, HK, HN), pp. 1116–1124.
- ICML-c2-2014-SatoN #analysis #approximate #equation #probability #process #using
- Approximation Analysis of Stochastic Gradient Langevin Dynamics by using Fokker-Planck Equation and Ito Process (IS, HN), pp. 982–990.
- ICML-2012-SatoN
- Rethinking Collapsed Variational Bayes Inference for LDA (IS, HN), p. 101.
- KDD-2012-SatoKN #process
- Practical collapsed variational bayes inference for hierarchical dirichlet process (IS, KK, HN), pp. 105–113.
- KDD-2010-SatoN #modelling #process #topic #using
- Topic models with power-law using Pitman-Yor process (IS, HN), pp. 673–682.
- KDD-2010-YangNSS #data mining #mining #privacy
- Collusion-resistant privacy-preserving data mining (BY, HN, IS, JS), pp. 483–492.
- SIGIR-2010-YoshidaIOSN #ambiguity
- Person name disambiguation by bootstrapping (MY, MI, SO, IS, HN), pp. 10–17.
- KDD-2008-SatoYN #graph #information management #parametricity #semantics #using #word
- Knowledge discovery of semantic relationships between words using nonparametric bayesian graph model (IS, MY, HN), pp. 587–595.
- KDD-2007-SatoN #documentation #information management #multi #parametricity #topic #using
- Knowledge discovery of multiple-topic document using parametric mixture model with dirichlet prior (IS, HN), pp. 590–598.