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2020 – today
- 2024
[j15]Ryosuke Shimmura
, Joe Suzuki:
Estimation of a Simple Structure in a Multidimensional IRT Model Using Structure Regularization. Entropy 26(1): 44 (2024)- [j14]Ryosuke Shimmura, Joe Suzuki:
Newton-Type Methods with the Proximal Gradient Step for Sparse Estimation. Oper. Res. Forum 5(2): 27 (2024) - [c27]Joe Suzuki, Tianle Yang:
Generalization of LiNGAM that Allows Confounding. ISIT 2024: 3540-3545 - [i15]Tianle Yang, Kuang-Yao Lee, Kun Zhang, Joe Suzuki:
Functional Linear Non-Gaussian Acyclic Model for Causal Discovery. CoRR abs/2401.09641 (2024) - [i14]Joe Suzuki, Tianle Yang:
Generalization of LiNGAM that allows confounding. CoRR abs/2401.16661 (2024) - [i13]Lirui Liu, Joe Suzuki:
Learning under Singularity: An Information Criterion improving WBIC and sBIC. CoRR abs/2402.12762 (2024) - [i12]Hongming Huang, Joe Suzuki:
An Efficient Procedure for Computing Bayesian Network Structure Learning. CoRR abs/2407.17072 (2024) - 2023
- [b4]Joe Suzuki:
WAIC and WBIC with R Stan - 100 Exercises for Building Logic. Springer 2023, ISBN 978-981-99-3837-7, pp. 1-239 - [j13]Bingyuan Zhang, Joe Suzuki:
Extending Hilbert-Schmidt Independence Criterion for Testing Conditional Independence. Entropy 25(3): 425 (2023) - [i11]Tianle Yang, Joe Suzuki:
Dropout Drops Double Descent. CoRR abs/2305.16179 (2023) - 2022
- [b3]Joe Suzuki:
Kernel Methods for Machine Learning with Math and R - 100 Exercises for Building Logic. Springer 2022, ISBN 978-981-19-0397-7, pp. 1-196 - [c26]Tianle Yang, Joe Suzuki:
The Functional LiNGAM. PGM 2022: 25-36 - 2021
- [b2]Joe Suzuki:
Sparse Estimation with Math and R - 100 Exercises for Building Logic. Springer 2021, ISBN 978-981-16-1445-3, pp. 1-234 - [j12]Jie Chen, Ryosuke Shimmura, Joe Suzuki:
Efficient Proximal Gradient Algorithms for Joint Graphical Lasso. Entropy 23(12): 1623 (2021) - [i10]Joe Suzuki, Yusuke Inaoka:
Causal Order Identification to Address Confounding: Binary Variables. CoRR abs/2108.04947 (2021) - 2020
- [b1]Joe Suzuki:
Statistical Learning with Math and R - 100 Exercises for Building Logic. Springer 2020, ISBN 978-981-15-7567-9, pp. 1-214
2010 – 2019
- 2019
- [c25]Joe Suzuki:
Mutual Information Estimation: Independence Detection and Consistency. ISIT 2019: 2514-2518 - 2018
- [j11]Joe Suzuki:
Forest Learning From Data and its Universal Coding. IEEE Trans. Inf. Theory 64(11): 7349-7358 (2018) - [i9]Joe Suzuki:
Forest Learning from Data and its Universal Coding. CoRR abs/1808.00566 (2018) - 2017
- [j10]Joe Suzuki:
A novel Chow-Liu algorithm and its application to gene differential analysis. Int. J. Approx. Reason. 80: 1-18 (2017) - [j9]Joe Suzuki:
An Efficient Bayesian Network Structure Learning Strategy. New Gener. Comput. 35(1): 105-124 (2017) - [c24]Joe Suzuki, Antti Hyttinen, Brandon M. Malone:
Advanced Methodologies for Bayesian Networks 2017: Preface. AMBN 2017: 1-2 - [c23]Joe Suzuki, Jun Kawahara:
Branch and Bound for Regular Bayesian Network Structure Learing. UAI 2017 - [e2]Antti Hyttinen, Joe Suzuki, Brandon M. Malone:
Proceedings of the 3rd Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2017, Kyoto, Japan, September 20-22, 2017. Proceedings of Machine Learning Research 73, PMLR 2017 [contents] - 2016
- [j8]Joe Suzuki:
An Estimator of Mutual Information and its Application to Independence Testing. Entropy 18(4): 109 (2016) - [c22]Joe Suzuki:
Structure learning and universal coding when missing values exist. ISIT 2016: 1725-1729 - [i8]Joe Suzuki:
A Theoretical Analysis of the BDeu Scores in Bayesian Network Structure Learning. CoRR abs/1607.04427 (2016) - 2015
- [j7]Joe Suzuki:
Consistency of Learning Bayesian Network Structures with Continuous Variables: An Information Theoretic Approach. Entropy 17(8): 5752-5770 (2015) - [c21]Joe Suzuki:
Efficiently Learning Bayesian Network Structures Based on the B&B Strategy: A Theoretical Analysis. AMBN@JSAI-isAI 2015: 1-14 - [c20]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: 234-249 - [e1]Joe Suzuki, Maomi Ueno:
Advanced Methodologies for Bayesian Networks - Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings. Lecture Notes in Computer Science 9505, Springer 2015, ISBN 978-3-319-28378-4 [contents] - 2014
- [c19]Joe Suzuki:
Learning Bayesian Network Structures When Discrete and Continuous Variables Are Present. Probabilistic Graphical Models 2014: 471-486 - [i7]Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara:
Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM. CoRR abs/1401.5636 (2014) - [i6]Joe Suzuki:
Universal Bayesian Measures and Universal Histogram Sequences. CoRR abs/1405.6033 (2014) - 2013
- [c18]Joe Suzuki:
Universal Bayesian measures. ISIT 2013: 644-648 - [i5]Joe Suzuki:
A Construction of Bayesian Networks from Databases Based on an MDL Principle. CoRR abs/1303.1486 (2013) - 2012
- [j6]Takanori Ayano, Joe Suzuki:
On d-Asymptotics for High-Dimensional Discriminant Analysis with Different Variance-Covariance Matrices. IEICE Trans. Inf. Syst. 95-D(12): 3106-3108 (2012) - [c17]Joe Suzuki:
Bayesian Network Structure Estimation Based on the Bayesian/MDL Criteria When Both Discrete and Continuous Variables Are Present. DCC 2012: 307-316 - [c16]Joe Suzuki:
Bayesian criteria based on universal measures. ISITA 2012: 71-75 - [i4]Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara:
Discovering causal structures in binary exclusive-or skew acyclic models. CoRR abs/1202.3736 (2012) - 2011
- [c15]Joe Suzuki:
MDL/Bayesian Criteria Based on Universal Coding/Measure. Algorithmic Probability and Friends 2011: 399-410 - [c14]Joe Suzuki:
The Universal Measure for General Sources and Its Application to MDL/Bayesian Criteria. DCC 2011: 478 - [c13]Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara:
Discovering causal structures in binary exclusive-or skew acyclic models. UAI 2011: 373-382 - 2010
- [c12]Joe Suzuki:
A Generalization of the Chow-Liu Algorithm and its Application to Artificial Intelligence. IC-AI 2010: 478-486 - [i3]Joe Suzuki:
A Generalization of the Chow-Liu Algorithm and its Application to Statistical Learning. CoRR abs/1002.2240 (2010) - [i2]Joe Suzuki:
Nonparametric Estimation and On-Line Prediction for General Stationary Ergodic Sources. CoRR abs/1002.4453 (2010)
2000 – 2009
- 2006
- [j5]Joe Suzuki:
On Strong Consistency of Model Selection in Classification. IEEE Trans. Inf. Theory 52(11): 4767-4774 (2006) - 2005
- [c11]U. Chandimal de Silva, Joe Suzuki:
On the stationary distribution of GAs with fixed crossover probability. GECCO 2005: 1147-1151 - 2004
- [j4]Joe Suzuki, Boris Ryabko:
Coding combinatorial sources with costs. IEEE Trans. Inf. Theory 50(5): 925-928 (2004) - [i1]Joe Suzuki:
Generalizing Kedlaya's order counting based on Miura Theory. IACR Cryptol. ePrint Arch. 2004: 129 (2004) - 2003
- [j3]Joe Suzuki:
Universal prediction and universal coding. Syst. Comput. Jpn. 34(6): 1-11 (2003) - 2000
- [c10]Ryuichi Harasawa, Joe Suzuki:
Fast Jacobian Group Arithmetic on CabCurves. ANTS 2000: 359-376
1990 – 1999
- 1999
- [c9]Junji Shikata, Yuliang Zheng, Joe Suzuki, Hideki Imai:
Optimizing the Menezes-Okamoto-Vanstone (MOV) Algorithm for Non-supersingular Elliptic Curves. ASIACRYPT 1999: 86-102 - [c8]Ryuichi Harasawa, Junji Shikata, Joe Suzuki, Hideki Imai:
Comparing the MOV and FR Reductions in Elliptic Curve Cryptography. EUROCRYPT 1999: 190-205 - 1998
- [j2]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): 95-102 (1998) - [c7]Joseph H. Silverman, Joe Suzuki:
Elliptic Curve Discrete Logarithms and the Index Calculus. ASIACRYPT 1998: 110-125 - 1997
- [c6]Joe Suzuki:
On the Error Probability of Model Selection for Classification. AISTATS 1997: 513-520 - 1996
- [c5]Joe Suzuki:
A CTW Scheme for Non-Tree Sources. Data Compression Conference 1996: 454 - [c4]Joe Suzuki:
A Further Result on the Markov Chain Model of Genetic Algorithms and Its Application to a Simulated Annealing-like Strategy. FOGA 1996: 53-72 - [c3]Joe Suzuki:
Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: An Efficient Algorithm Using the B & B Technique. ICML 1996: 462-470 - 1995
- [j1]Joe Suzuki:
A Markov chain analysis on simple genetic algorithms. IEEE Trans. Syst. Man Cybern. 25(4): 655-659 (1995) - 1993
- [c2]Joe Suzuki:
A Markov Chain Analysis on A Genetic Algorithm. ICGA 1993: 146-154 - [c1]Joe Suzuki:
A Construction of Bayesian Networks from Databases Based on an MDL Principle. UAI 1993: 266-273
Coauthor Index
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