J. Suzuki

发表

Some Notes on Universal Noiseless Coding

J. Suzuki, 1995 .

Fast Jacobian Group Arithmetic on CabCurves

Joe Suzuki, Ryuichi Harasawa, J. Suzuki, 2000, ANTS.

Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: Basic Properties

J. Suzuki, 1999 .

A Markov Chain Analysis of Genetic Algorithms: Large Deviation Principle Approach

Joe Suzuki, J. Suzuki, 2010, Journal of Applied Probability.

On uncertain logic based upon information theory

Shigeichi Hirasawa, Toshiyasu Matsushima, Hiroshige Inazumi, 1988 .

Realizing the Menezes-Okamoto-Vanstone (MOV) Reduction Efficiently for Ordinary Elliptic Curves

J. Suzuki, H. Imai, Yuliang Zheng, 2000 .

On the error probability of model selection for classification

J. Suzuki, 1997, Proceedings of IEEE International Symposium on Information Theory.

Logistic regression and Ising networks: prediction and estimation when violating lasso assumptions

J. Suzuki, 2018 .

A generalized version of Chow-Liu algorithm for data mining

J. Suzuki, Yuu Ishida, 2009 .

Linear Algebra

J. Suzuki, 2020, Statistical Learning with Math and Python.

Identifiability of an Integer Modular Acyclic Additive Noise Model and its Causal Structure Discovery

J. Suzuki, Shohei Shimizu, T. Washio, 2014, 1401.5625.

Tighter bounds on universal noiseless coding for finite sequences

J. Suzuki, 1994, Proceedings of 1994 IEEE International Symposium on Information Theory.

Paper Learning Bayesian Belief Networks Based on the Mdl Prin- Ciple: an Eecient Algorithm Using the Branch and Bound Technique

J. Suzuki, 1998 .

An extension on learning Bayesian belief networks based on MDL principle

J. Suzuki, 1995, Proceedings of 1995 IEEE International Symposium on Information Theory.

The Hannan-Quinn Proposition for Linear Regression

J. Suzuki, 2010, 1012.4276.

The Hannan-Quinn proposition is true for learning linear regression and Bayesian network structures

J. Suzuki, 2009 .

Fused Lasso

J. Suzuki, 2021, Sparse Estimation with Math and R.

Dirichlet Bayesian network scores and the maximum relative entropy principle

J. Suzuki, 2018 .

Klein's Fundamental 2-Form of Second Kind for the Cab Curves

J. Suzuki, 2017, 1701.00931.

A Fast Jacobian Group Arithmetic Scheme for Algebraic Curve Cryptography

J. Suzuki, Ryuichi Harasawa, 2001 .

Inductive inference scheme at a finite stage of process from a view point of source coding

Shigeichi Hirasawa, Toshiyasu Matsushima, H. Inazumi, 1990 .

The Functional LiNGAM

J. Suzuki, Tianle Yang, 2022, PGM.

An Efficient Algorithm for Convex Biclustering

J. Suzuki, Jie Chen, 2021, Mathematics.

On Strong Consistency of Model Selection in Classification

Joe Suzuki, J. Suzuki, 2006, IEEE Transactions on Information Theory.

Universal Bayesian measures

Joe Suzuki, J. Suzuki, 2013, 2013 IEEE International Symposium on Information Theory.

Bayesian criteria based on universal measures

Joe Suzuki, J. Suzuki, 2012, 2012 International Symposium on Information Theory and its Applications.

Causal order identification to address confounding: binary variables pdf

Joe Suzuki, Yusuke Inaoka, J. Suzuki, 2021, Behaviormetrika.

Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM pdf

Akihiro Yamamoto, Joe Suzuki, Yoshinobu Kawahara, 2014, ArXiv.

Nonparametric Estimation and On-Line Prediction for General Stationary Ergodic Sources pdf

Joe Suzuki, J. Suzuki, 2010, ArXiv.

A Construction of Bayesian Networks from Databases Based on an MDL Principle pdf

Joe Suzuki, J. Suzuki, 1993, UAI.

On the stationary distribution of GAs with fixed crossover probability

Joe Suzuki, U. Chandimal de Silva, J. Suzuki, 2005, GECCO '05.

A Markov chain analysis on simple genetic algorithms

Joe Suzuki, J. Suzuki, 1995, IEEE Trans. Syst. Man Cybern..

A Further Result on the Markov Chain Model of Genetic Algorithms and Its Application to a Simulated Annealing-like Strategy

Joe Suzuki, J. Suzuki, 1998, FOGA.

Elliptic Curve Discrete Logarithms and the Index Calculus

Joe Suzuki, Joseph H. Silverman, J. Suzuki, 1998, ASIACRYPT.

A theoretical analysis of the BDeu scores in Bayesian network structure learning

Joe Suzuki, J. Suzuki, 2016, ArXiv.

Consistency of Learning Bayesian Network Structures with Continuous Variables: An Information Theoretic Approach

Joe Suzuki, J. Suzuki, 2015, Entropy.

Extending Hilbert–Schmidt Independence Criterion for Testing Conditional Independence

J. Suzuki, Bingyuan Zhang, 2023, Entropy.

An Estimator of Mutual Information and its Application to Independence Testing

Joe Suzuki, J. Suzuki, 2016, Entropy.

Comparing the MOV and FR Reductions in Elliptic Curve Cryptography

Junji Shikata, Hideki Imai, Joe Suzuki, 1999, EUROCRYPT.

A Relationship between Contex Tree Weighting and General Model Weighting Techniques for Tree Sources

Joe Suzuki, J. Suzuki, 1998 .

Why BDeu? Regular Bayesian network structure learning with discrete and continuous variables

Joe Suzuki, J. Suzuki, 2021, WIREs Computational Statistics.

Efficient Proximal Gradient Algorithms for Joint Graphical Lasso

Joe Suzuki, Jie Chen, Ryosuke Shimmura, 2021, Entropy.

Dropout Drops Double Descent

Tianbao Yang, J. Suzuki, 2023, ArXiv.

Discovering causal structures in binary exclusive-or skew acyclic models

Akihiro Yamamoto, Joe Suzuki, Yoshinobu Kawahara, 2011, UAI.

Regularization

J. Suzuki, 2021, Statistical Learning with Math and Python.

A novel Chow-Liu algorithm and its application to gene differential analysis

Joe Suzuki, J. Suzuki, 2017, Int. J. Approx. Reason..

Learning Bayesian Belief Networks Based on the MDL Principle : An Efficient Algorithm Using the Branch and Bound Technique

Joe Suzuki, J. Suzuki, 1999 .

Converting ADMM to a proximal gradient for efficient sparse estimation

J. Suzuki, Ryosuke Shimmura, 2021, Japanese Journal of Statistics and Data Science.

Converting ADMM to a Proximal Gradient for Convex Optimization Problems

J. Suzuki, Ryosuke Shimmura, 2021 .

Bayesian Network Structure Estimation Based on the Bayesian/MDL Criteria When Both Discrete and Continuous Variables Are Present

Joe Suzuki, J. Suzuki, 2012, 2012 Data Compression Conference.

An Efficient Bayesian Network Structure Learning Strategy

Joe Suzuki, J. Suzuki, 2016, New Generation Computing.

Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: An Efficient Algorithm Using the B & B Technique

Joe Suzuki, J. Suzuki, 1996, ICML.

A theoretical analysis of the BDeu scores in Bayesian network structure learning

J. Suzuki, 2016, Behaviormetrika.

Efficient Bayesian Network Structure Learning for Maximizing the Posterior Probability (特集「命題論理の充足可能性問題SATと応用技術」および一般)

Joe Suzuki, J. Suzuki, 2016 .

Branch and Bound for Regular Bayesian Network Structure Learing

Joe Suzuki, Jun Kawahara, J. Suzuki, 2017, UAI.