A Markov Chain Analysis on A Genetic Algorithm. - Joe Suzuki (ICGA, 1993)

A Construction of Bayesian Networks from Databases Based on an MDL Principle. - Joe Suzuki (UAI, 1993)

A Markov chain analysis on simple genetic algorithms. - Joe Suzuki (IEEE Trans. Syst. Man Cybern., 1995)

A CTW Scheme for Non-Tree Sources. - Joe Suzuki (Data Compression Conference, 1996)

Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: An Efficient Algorithm Using the B & B Technique. - Joe Suzuki (ICML, 1996)

On the Error Probability of Model Selection for Classification. - Joe Suzuki (AISTATS, 1997)

Elliptic Curve Discrete Logarithms and the Index Calculus. - Joseph H. Silverman; Joe Suzuki (ASIACRYPT, 1998)

A further result on the Markov chain model of genetic algorithms and its application to a simulated annealing-like strategy. - Joe Suzuki (IEEE Trans. Syst. Man Cybern. Part B, 1998)

Optimizing the Menezes-Okamoto-Vanstone (MOV) Algorithm for Non-supersingular Elliptic Curves. - Junji Shikata; Yuliang Zheng 001; Joe Suzuki; Hideki Imai (ASIACRYPT, 1999)

Comparing the MOV and FR Reductions in Elliptic Curve Cryptography. - Ryuichi Harasawa; Junji Shikata; Joe Suzuki; Hideki Imai (EUROCRYPT, 1999)

Fast Jacobian Group Arithmetic on CabCurves. - Ryuichi Harasawa; Joe Suzuki (ANTS, 2000)

Universal prediction and universal coding. - Joe Suzuki (Syst. Comput. Jpn., 2003)

Generalizing Kedlaya's order counting based on Miura Theory. - Joe Suzuki (IACR Cryptol. ePrint Arch., 2004)

Coding combinatorial sources with costs. - Joe Suzuki; Boris Ryabko (IEEE Trans. Inf. Theory, 2004)

On the stationary distribution of GAs with fixed crossover probability. - U. Chandimal de Silva; Joe Suzuki (GECCO, 2005)

On Strong Consistency of Model Selection in Classification. - Joe Suzuki (IEEE Trans. Inf. Theory, 2006)

A Generalization of the Chow-Liu Algorithm and its Application to Artificial Intelligence. - Joe Suzuki (IC-AI, 2010)

A Generalization of the Chow-Liu Algorithm and its Application to Statistical Learning - Joe Suzuki (CoRR, 2010)

Nonparametric Estimation and On-Line Prediction for General Stationary Ergodic Sources - Joe Suzuki (CoRR, 2010)

MDL/Bayesian Criteria Based on Universal Coding/Measure. - Joe Suzuki (Algorithmic Probability and Friends, 2011)

The Universal Measure for General Sources and Its Application to MDL/Bayesian Criteria. - Joe Suzuki (DCC, 2011)

Discovering causal structures in binary exclusive-or skew acyclic models. - Takanori Inazumi; Takashi Washio; Shohei Shimizu; Joe Suzuki; Akihiro Yamamoto; Yoshinobu Kawahara (UAI, 2011)

Bayesian Network Structure Estimation Based on the Bayesian/MDL Criteria When Both Discrete and Continuous Variables Are Present. - Joe Suzuki (DCC, 2012)

Bayesian criteria based on universal measures. - Joe Suzuki (ISITA, 2012)

On d-Asymptotics for High-Dimensional Discriminant Analysis with Different Variance-Covariance Matrices. - Takanori Ayano; Joe Suzuki (IEICE Trans. Inf. Syst., 2012)

Universal Bayesian measures. - Joe Suzuki (ISIT, 2013)

Learning Bayesian Network Structures When Discrete and Continuous Variables Are Present. - Joe Suzuki (Probabilistic Graphical Models, 2014)

Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM. - Takanori Inazumi; Takashi Washio; Shohei Shimizu; Joe Suzuki; Akihiro Yamamoto; Yoshinobu Kawahara (CoRR, 2014)

Universal Bayesian Measures and Universal Histogram Sequences. - Joe Suzuki (CoRR, 2014)

Efficiently Learning Bayesian Network Structures Based on the B&B Strategy: A Theoretical Analysis. - Joe Suzuki (AMBN@JSAI-isAI, 2015)

Forest Learning Based on the Chow-Liu Algorithm and Its Application to Genome Differential Analysis: A Novel Mutual Information Estimation. - Joe Suzuki (AMBN@JSAI-isAI, 2015)

Consistency of Learning Bayesian Network Structures with Continuous Variables: An Information Theoretic Approach. - Joe Suzuki (Entropy, 2015)

Structure learning and universal coding when missing values exist. - Joe Suzuki (ISIT, 2016)

A Theoretical Analysis of the BDeu Scores in Bayesian Network Structure Learning. - Joe Suzuki (CoRR, 2016)

An Estimator of Mutual Information and its Application to Independence Testing. - Joe Suzuki (Entropy, 2016)

Advanced Methodologies for Bayesian Networks 2017: Preface. - Joe Suzuki; Antti Hyttinen; Brandon M. Malone (AMBN, 2017)

Branch and Bound for Regular Bayesian Network Structure Learing. - Joe Suzuki; Jun Kawahara (UAI, 2017)

A novel Chow-Liu algorithm and its application to gene differential analysis. - Joe Suzuki (Int. J. Approx. Reason., 2017)

An Efficient Bayesian Network Structure Learning Strategy. - Joe Suzuki (New Gener. Comput., 2017)

Forest Learning From Data and its Universal Coding. - Joe Suzuki (IEEE Trans. Inf. Theory, 2018)

Mutual Information Estimation: Independence Detection and Consistency. - Joe Suzuki (ISIT, 2019)

Statistical Learning with Math and R - 100 Exercises for Building Logic - Joe Suzuki (Springer, 2020)

Sparse Estimation with Math and R - 100 Exercises for Building Logic - Joe Suzuki (Springer, 2021)

Causal Order Identification to Address Confounding: Binary Variables. - Joe Suzuki; Yusuke Inaoka (CoRR, 2021)

Efficient Proximal Gradient Algorithms for Joint Graphical Lasso. - Jie Chen; Ryosuke Shimmura; Joe Suzuki (Entropy, 2021)

Kernel Methods for Machine Learning with Math and R - 100 Exercises for Building Logic - Joe Suzuki (Springer, 2022)

The Functional LiNGAM. - Tianle Yang; Joe Suzuki (PGM, 2022)

WAIC and WBIC with R Stan - 100 Exercises for Building Logic - Joe Suzuki (Springer, 2023)

Dropout Drops Double Descent. - Tianle Yang; Joe Suzuki (CoRR, 2023)

Extending Hilbert-Schmidt Independence Criterion for Testing Conditional Independence. - Bingyuan Zhang; Joe Suzuki (Entropy, 2023)

Generalization of LiNGAM that Allows Confounding. - Joe Suzuki; Tianle Yang (ISIT, 2024)

Functional Linear Non-Gaussian Acyclic Model for Causal Discovery. - Tianle Yang; Kuang-Yao Lee; Kun Zhang; Joe Suzuki (CoRR, 2024)

Learning under Singularity: An Information Criterion improving WBIC and sBIC. - Lirui Liu; Joe Suzuki (CoRR, 2024)

An Efficient Procedure for Computing Bayesian Network Structure Learning. - Hongming Huang; Joe Suzuki (CoRR, 2024)

Estimation of a Simple Structure in a Multidimensional IRT Model Using Structure Regularization. - Ryosuke Shimmura; Joe Suzuki (Entropy, 2024)

Newton-Type Methods with the Proximal Gradient Step for Sparse Estimation. - Ryosuke Shimmura; Joe Suzuki (Oper. Res. Forum, 2024)

Estimation of the Learning Coefficient Using Empirical Loss. - Tatsuyoshi Takio; Joe Suzuki (CoRR, 2025)