Joe Suzuki

Publication Activity (10 Years)

Years Active: 1993-2024
Publications (10 Years): 21

Top Topics

Mutual Information

Bayesian Network Structures

Continuous Variables

Conditional Independence

Top Venues

Publications

Joe Suzuki, Tianle Yang
Generalization of LiNGAM that allows confounding. CoRR (2024)
Hongming Huang, Joe Suzuki
An Efficient Procedure for Computing Bayesian Network Structure Learning. CoRR (2024)
Lirui Liu, Joe Suzuki
Learning under Singularity: An Information Criterion improving WBIC and sBIC. CoRR (2024)
Ryosuke Shimmura, Joe Suzuki
Newton-Type Methods with the Proximal Gradient Step for Sparse Estimation. Oper. Res. Forum 5 (2) (2024)
Tianle Yang, Kuang-Yao Lee, Kun Zhang, Joe Suzuki
Functional Linear Non-Gaussian Acyclic Model for Causal Discovery. CoRR (2024)
Ryosuke Shimmura, Joe Suzuki
Estimation of a Simple Structure in a Multidimensional IRT Model Using Structure Regularization. Entropy 26 (1) (2024)
Bingyuan Zhang, Joe Suzuki
Extending Hilbert-Schmidt Independence Criterion for Testing Conditional Independence. Entropy 25 (3) (2023)
Tianle Yang, Joe Suzuki
Dropout Drops Double Descent. CoRR (2023)
Tianle Yang, Joe Suzuki
The Functional LiNGAM. PGM (2022)
Jie Chen, Ryosuke Shimmura, Joe Suzuki
Efficient Proximal Gradient Algorithms for Joint Graphical Lasso. Entropy 23 (12) (2021)
Joe Suzuki, Yusuke Inaoka
Causal Order Identification to Address Confounding: Binary Variables. CoRR (2021)
Joe Suzuki
Mutual Information Estimation: Independence Detection and Consistency. ISIT (2019)
Joe Suzuki
Forest Learning From Data and its Universal Coding. IEEE Trans. Inf. Theory 64 (11) (2018)
Joe Suzuki
Forest Learning from Data and its Universal Coding. CoRR (2018)
Joe Suzuki, Jun Kawahara
Branch and Bound for Regular Bayesian Network Structure Learing. UAI (2017)
Joe Suzuki, Antti Hyttinen, Brandon M. Malone
Advanced Methodologies for Bayesian Networks 2017: Preface. AMBN (2017)
Joe Suzuki
A novel Chow-Liu algorithm and its application to gene differential analysis. Int. J. Approx. Reason. 80 (2017)
Joe Suzuki
An Efficient Bayesian Network Structure Learning Strategy. New Gener. Comput. 35 (1) (2017)
Joe Suzuki
Structure learning and universal coding when missing values exist. ISIT (2016)
Joe Suzuki
An Estimator of Mutual Information and its Application to Independence Testing. Entropy 18 (4) (2016)
Joe Suzuki
A Theoretical Analysis of the BDeu Scores in Bayesian Network Structure Learning. CoRR (2016)
Joe Suzuki
Forest Learning Based on the Chow-Liu Algorithm and Its Application to Genome Differential Analysis: A Novel Mutual Information Estimation. AMBN@JSAI-isAI (2015)
Joe Suzuki
Efficiently Learning Bayesian Network Structures Based on the B&B Strategy: A Theoretical Analysis. AMBN@JSAI-isAI (2015)
Joe Suzuki
Consistency of Learning Bayesian Network Structures with Continuous Variables: An Information Theoretic Approach. Entropy 17 (8) (2015)
Joe Suzuki
Learning Bayesian Network Structures When Discrete and Continuous Variables Are Present. Probabilistic Graphical Models (2014)
Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara
Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM. CoRR (2014)
Joe Suzuki
Universal Bayesian Measures and Universal Histogram Sequences. CoRR (2014)
Joe Suzuki
A Construction of Bayesian Networks from Databases Based on an MDL Principle CoRR (2013)
Joe Suzuki
Universal Bayesian measures. ISIT (2013)
Joe Suzuki
Bayesian Network Structure Estimation Based on the Bayesian/MDL Criteria When Both Discrete and Continuous Variables Are Present. DCC (2012)
Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara
Discovering causal structures in binary exclusive-or skew acyclic models CoRR (2012)
Takanori Ayano, Joe Suzuki
-Asymptotics for High-Dimensional Discriminant Analysis with Different Variance-Covariance Matrices. IEICE Trans. Inf. Syst. (12) (2012)
Joe Suzuki
Bayesian criteria based on universal measures. ISITA (2012)
Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara
Discovering causal structures in binary exclusive-or skew acyclic models. UAI (2011)
Joe Suzuki
The Universal Measure for General Sources and Its Application to MDL/Bayesian Criteria. DCC (2011)
Joe Suzuki
MDL/Bayesian Criteria Based on Universal Coding/Measure. Algorithmic Probability and Friends (2011)
Joe Suzuki
A Generalization of the Chow-Liu Algorithm and its Application to Artificial Intelligence. IC-AI (2010)
Joe Suzuki
Nonparametric Estimation and On-Line Prediction for General Stationary Ergodic Sources CoRR (2010)
Joe Suzuki
A Generalization of the Chow-Liu Algorithm and its Application to Statistical Learning CoRR (2010)
Joe Suzuki
On Strong Consistency of Model Selection in Classification. IEEE Trans. Inf. Theory 52 (11) (2006)
U. Chandimal de Silva, Joe Suzuki
On the stationary distribution of GAs with fixed crossover probability. GECCO (2005)
Joe Suzuki
Generalizing Kedlaya's order counting based on Miura Theory. IACR Cryptol. ePrint Arch. 2004 (2004)
Joe Suzuki, Boris Ryabko
Coding combinatorial sources with costs. IEEE Trans. Inf. Theory 50 (5) (2004)
Joe Suzuki
Universal prediction and universal coding. Systems and Computers in Japan 34 (6) (2003)
Ryuichi Harasawa, Joe Suzuki
Curves. ANTS (2000)
Ryuichi Harasawa, Junji Shikata, Joe Suzuki, Hideki Imai
Comparing the MOV and FR Reductions in Elliptic Curve Cryptography. EUROCRYPT (1999)
Junji Shikata, Yuliang Zheng, Joe Suzuki, Hideki Imai
Optimizing the Menezes-Okamoto-Vanstone (MOV) Algorithm for Non-supersingular Elliptic Curves. ASIACRYPT (1999)
Joe Suzuki
A further result on the Markov chain model of genetic algorithms and its application to a simulated annealing-like strategy. IEEE Trans. Syst. Man Cybern. Part B 28 (1) (1998)
Joseph H. Silverman, Joe Suzuki
Elliptic Curve Discrete Logarithms and the Index Calculus. ASIACRYPT (1998)
Joe Suzuki
On the Error Probability of Model Selection for Classification. AISTATS (1997)
Joe Suzuki
Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: An Efficient Algorithm Using the B & B Technique. ICML (1996)
Joe Suzuki
A CTW Scheme for Non-Tree Sources. Data Compression Conference (1996)
Joe Suzuki
A Further Result on the Markov Chain Model of Genetic Algorithms and Its Application to a Simulated Annealing-like Strategy. FOGA (1996)
Joe Suzuki
A Markov chain analysis on simple genetic algorithms. IEEE Trans. Syst. Man Cybern. 25 (4) (1995)
Joe Suzuki
A Markov Chain Analysis on A Genetic Algorithm. ICGA (1993)
Joe Suzuki
A Construction of Bayesian Networks from Databases Based on an MDL Principle. UAI (1993)