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2020 – today
- 2024
[j28]Atsuyoshi Nakamura
:
Query learning algorithm for ordered multi-terminal binary decision diagrams. Discret. Appl. Math. 352: 69-87 (2024)- [j27]Tatsuya Hayashi, Naoki Ito, Koji Tabata, Atsuyoshi Nakamura, Katsumasa Fujita, Yoshinori Harada, Tamiki Komatsuzaki:
Gaussian process classification bandits. Pattern Recognit. 149: 110224 (2024) - 2023
- [c56]Koji Tabata, Junpei Komiyama, Atsuyoshi Nakamura, Tamiki Komatsuzaki:
Posterior Tracking Algorithm for Classification Bandits. AISTATS 2023: 10994-11022 - [c55]Xuyang Cao, Yang Cao, Primal Pappachan, Atsuyoshi Nakamura, Masatoshi Yoshikawa:
Differentially Private Streaming Data Release Under Temporal Correlations via Post-processing. DBSec 2023: 184-200 - [i12]Atsuyoshi Nakamura:
Query Learning Algorithm for Ordered Multi-Terminal Binary Decision Diagrams. CoRR abs/2303.03195 (2023) - [i11]Xuyang Cao, Yang Cao, Primal Pappachan, Atsuyoshi Nakamura, Masatoshi Yoshikawa:
Differentially Private Streaming Data Release under Temporal Correlations via Post-processing. CoRR abs/2306.13293 (2023) - [i10]Yaxiong Liu, Atsuyoshi Nakamura, Kohei Hatano, Eiji Takimoto:
Pure exploration in multi-armed bandits with low rank structure using oblivious sampler. CoRR abs/2306.15856 (2023) - 2022
- [j26]Atsuyoshi Nakamura, Tatsuya Hayashi:
Efficient alignment-based average delay time estimation in fluctuating delayed propagation. Array 15: 100240 (2022) - [c54]Kosuke Chinone, Atsuyoshi Nakamura:
An Explainable Recommendation Based on Acyclic Paths in an Edge-Colored Graph. AI 2022: 40-52 - [c53]Xuyang Cao, Yang Cao, Masatoshi Yoshikawa, Atsuyoshi Nakamura:
Boosting Utility of Differentially Private Streaming Data Release under Temporal Correlations. IEEE Big Data 2022: 6605-6607 - [i9]Hideaki Kuratsu, Atsuyoshi Nakamura:
Slimmable Pruned Neural Networks. CoRR abs/2212.03415 (2022) - [i8]Atsuyoshi Nakamura, Kento Sakurada:
Simplification of Forest Classifiers and Regressors. CoRR abs/2212.07103 (2022) - [i7]Tatsuya Hayashi, Naoki Ito, Koji Tabata, Atsuyoshi Nakamura, Katsumasa Fujita, Yoshinori Harada, Tamiki Komatsuzaki:
Gaussian Process Classification Bandits. CoRR abs/2212.13157 (2022) - 2021
- [j25]Atsuyoshi Nakamura:
Foreword. IEICE Trans. Inf. Syst. 104-D(3): 354 (2021) - [j24]Yuya Sugie, Yuki Yoshida, Normann Mertig, Takashi Takemoto, Hiroshi Teramoto, Atsuyoshi Nakamura, Ichigaku Takigawa, Shin-ichi Minato, Masanao Yamaoka, Tamiki Komatsuzaki:
Minor-embedding heuristics for large-scale annealing processors with sparse hardware graphs of up to 102, 400 nodes. Soft Comput. 25(3): 1731-1749 (2021) - [c52]Koji Tabata, Atsuyoshi Nakamura, Tamiki Komatsuzaki:
Classification Bandits: Classification Using Expected Rewards as Imperfect Discriminators. PAKDD (Workshops) 2021: 57-69 - 2020
- [j23]Koji Tabata, Atsuyoshi Nakamura, Junya Honda, Tamiki Komatsuzaki:
A bad arm existence checking problem: How to utilize asymmetric problem structure? Mach. Learn. 109(2): 327-372 (2020) - [c51]Atsuyoshi Nakamura, Ichigaku Takigawa, Hiroshi Mamitsuka:
Efficiently Enumerating Substrings with Statistically Significant Frequencies of Locally Optimal Occurrences in Gigantic String. AAAI 2020: 5240-5247 - [c50]Mitsuki Maekawa, Atsuyoshi Nakamura, Mineichi Kudo:
Data-Dependent Conversion to a Compact Integer-Weighted Representation of a Weighted Voting Classifier. ACML 2020: 241-256 - [c49]Taiga Ikeda, Kento Sakurada, Atsuyoshi Nakamura, Masato Motomura, Shinya Takamaeda-Yamazaki:
Hardware/Algorithm Co-optimization for Fully-Parallelized Compact Decision Tree Ensembles on FPGAs. ARC 2020: 345-357 - [i6]Yuya Sugie, Yuki Yoshida, Normann Mertig, Takashi Takemoto, Hiroshi Teramoto, Atsuyoshi Nakamura, Ichigaku Takigawa, Shin-ichi Minato, Masanao Yamaoka, Tamiki Komatsuzaki:
Minor-embedding heuristics for large-scale annealing processors with sparse hardware graphs of up to 102, 400 nodes. CoRR abs/2004.03819 (2020) - [i5]Tatsuya Hayashi, Atsuyoshi Nakamura:
Propagation Graph Estimation by Pairwise Alignment of Time Series Observation Sequences. CoRR abs/2005.04954 (2020)
2010 – 2019
- 2019
- [j22]Atsuyoshi Nakamura, David P. Helmbold, Manfred K. Warmuth:
Mistake bounds on the noise-free multi-armed bandit game. Inf. Comput. 269 (2019) - [j21]Hideaki Kano, Junya Honda, Kentaro Sakamaki, Kentaro Matsuura, Atsuyoshi Nakamura, Masashi Sugiyama:
Good arm identification via bandit feedback. Mach. Learn. 108(5): 721-745 (2019) - [c48]Atsuyoshi Nakamura, Kento Sakurada:
An Algorithm for Reducing the Number of Distinct Branching Conditions in a Decision Forest. ECML/PKDD (1) 2019: 578-589 - [c47]Aurélien Pélissier, Atsuyoshi Nakamura, Koji Tabata:
Feature selection as Monte-Carlo Search in Growing Single Rooted Directed Acyclic Graph by Best Leaf Identification. SDM 2019: 450-458 - [i4]Koji Tabata, Atsuyoshi Nakamura, Junya Honda, Tamiki Komatsuzaki:
A Bad Arm Existence Checking Problem. CoRR abs/1901.11200 (2019) - 2018
- [j20]Ryo Watanabe, Junpei Komiyama, Atsuyoshi Nakamura, Mineichi Kudo:
UCB-SC: A Fast Variant of KL-UCB-SC for Budgeted Multi-Armed Bandit Problem. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 101-A(3): 662-667 (2018) - [c46]Takashi Takemoto, Normann Mertig, Masato Hayashi, Saki Susa-Tanaka, Hiroshi Teramoto, Atsuyoshi Nakamura, Ichigaku Takigawa, Shin-ichi Minato, Tamiki Komatsuzaki, Masanao Yamaoka:
FPGA-Based QBoost with Large-Scale Annealing Processor and Accelerated Hyperparameter Search. ReConFig 2018: 1-8 - [c45]Yuya Sugie, Yuki Yoshida, Normann Mertig, Takashi Takemoto, Hiroshi Teramoto, Atsuyoshi Nakamura, Ichigaku Takigawa, Shin-ichi Minato, Masanao Yamaoka, Tamiki Komatsuzaki:
Graph Minors from Simulated Annealing for Annealing Machines with Sparse Connectivity. TPNC 2018: 111-123 - [i3]Aurélien Pélissier, Atsuyoshi Nakamura, Koji Tabata:
Feature selection as Monte-Carlo Search in Growing Single Rooted Directed Acyclic Graph by Best Leaf Identification. CoRR abs/1811.07531 (2018) - 2017
- [j19]Koji Tabata, Atsuyoshi Nakamura, Mineichi Kudo:
An Efficient Approximate Algorithm for the 1-Median Problem on a Graph. IEICE Trans. Inf. Syst. 100-D(5): 994-1002 (2017) - [j18]Ryo Watanabe, Junpei Komiyama, Atsuyoshi Nakamura, Mineichi Kudo:
KL-UCB-Based Policy for Budgeted Multi-Armed Bandits with Stochastic Action Costs. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 100-A(11): 2470-2486 (2017) - [i2]Hiroyuki Hanada, Mineichi Kudo, Atsuyoshi Nakamura:
On Practical Accuracy of Edit Distance Approximation Algorithms. CoRR abs/1701.06134 (2017) - 2016
- [j17]Atsuyoshi Nakamura, Ichigaku Takigawa, Hisashi Tosaka, Mineichi Kudo, Hiroshi Mamitsuka:
Mining approximate patterns with frequent locally optimal occurrences. Discret. Appl. Math. 200: 123-152 (2016) - [c44]Shunsuke Suzuki, Mineichi Kudo, Atsuyoshi Nakamura:
Sitting posture diagnosis using a pressure sensor mat. ISBA 2016: 1-6 - [c43]Atsuyoshi Nakamura, David P. Helmbold, Manfred K. Warmuth:
Noise Free Multi-armed Bandit Game. LATA 2016: 412-423 - 2015
- [j16]Ryo Watanabe, Atsuyoshi Nakamura, Mineichi Kudo:
An improved upper bound on the expected regret of UCB-type policies for a matching-selection bandit problem. Oper. Res. Lett. 43(6): 558-563 (2015) - [c42]Koji Tabata, Atsuyoshi Nakamura, Mineichi Kudo:
An Algorithm for Influence Maximization in a Two-Terminal Series Parallel Graph and its Application to a Real Network. Discovery Science 2015: 275-283 - [c41]Ayako Mikami, Mineichi Kudo, Atsuyoshi Nakamura:
Diversity Measures and Margin Criteria in Multi-class Majority Vote Ensemble. MCS 2015: 27-37 - 2014
- [j15]Koji Ouchi, Atsuyoshi Nakamura, Mineichi Kudo:
An efficient construction and application usefulness of rectangle greedy covers. Pattern Recognit. 47(3): 1459-1468 (2014) - [j14]Hiroyuki Hanada, Mineichi Kudo, Atsuyoshi Nakamura:
Average-case linear-time similar substring searching by the q-gram distance. Theor. Comput. Sci. 530: 23-41 (2014) - [c40]Atsuyoshi Nakamura:
A UCB-Like Strategy of Collaborative Filtering. ACML 2014 - 2013
- [j13]Atsuyoshi Nakamura, Tomoya Saito, Ichigaku Takigawa, Mineichi Kudo, Hiroshi Mamitsuka:
Fast algorithms for finding a minimum repetition representation of strings and trees. Discret. Appl. Math. 161(10-11): 1556-1575 (2013) - 2012
- [c39]Koji Tabata, Atsuyoshi Nakamura, Mineichi Kudo:
Fast Approximation Algorithm for the 1-Median Problem. Discovery Science 2012: 169-183 - [c38]Atsuyoshi Nakamura, Hisashi Tosaka, Mineichi Kudo:
Frequent Approximate Substring Pattern Mining Using Locally Optimal Occurrence Counting. IIAI-AAI 2012: 54-59 - 2011
- [j12]Takashi Saso, Kojiro Kobayashi, Atsuyoshi Nakamura:
On the possible patterns of inputs for block sorting in the Burrows-Wheeler transformation. Inf. Process. Lett. 111(12): 595-599 (2011) - [j11]Tetsuji Takahashi, Mineichi Kudo, Atsuyoshi Nakamura:
Construction of convex hull classifiers in high dimensions. Pattern Recognit. Lett. 32(16): 2224-2230 (2011) - [c37]Hiroyuki Hanada, Atsuyoshi Nakamura, Mineichi Kudo:
A practical comparison of edit distance approximation algorithms. GrC 2011: 231-236 - [c36]Koji Ouchi, Atsuyoshi Nakamura, Mineichi Kudo:
Efficient construction and usefulness of hyper-rectangle greedy covers. GrC 2011: 533-538 - [c35]Atsuyoshi Nakamura, Mineichi Kudo:
Packing Alignment: Alignment for Sequences of Various Length Events. PAKDD (2) 2011: 234-245 - 2010
- [c34]Taishi Uchiya, Atsuyoshi Nakamura, Mineichi Kudo:
Algorithms for Adversarial Bandit Problems with Multiple Plays. ALT 2010: 375-389 - [c33]Atsuyoshi Nakamura, Tomoya Saito, Ichigaku Takigawa, Hiroshi Mamitsuka, Mineichi Kudo:
Algorithms for Finding a Minimum Repetition Representation of a String. SPIRE 2010: 185-190
2000 – 2009
- 2009
- [j10]Ichigaku Takigawa, Mineichi Kudo, Atsuyoshi Nakamura:
Convex sets as prototypes for classifying patterns. Eng. Appl. Artif. Intell. 22(1): 101-108 (2009) - [c32]Tetsuji Takahashi, Mineichi Kudo, Atsuyoshi Nakamura:
Classifier Selection in a Family of Polyhedron Classifiers. CIARP 2009: 441-448 - [c31]Satoshi Shirai, Mineichi Kudo, Atsuyoshi Nakamura:
Comparison of Bagging and Boosting Algorithms on Sample and Feature Weighting. MCS 2009: 22-31 - 2008
- [j9]Yohji Shidara, Mineichi Kudo, Atsuyoshi Nakamura:
Classification Based on Consistent Itemset Rules. Trans. Mach. Learn. Data Min. 1(1): 17-30 (2008) - [c30]Atsuyoshi Nakamura, Mineichi Kudo:
What Sperner Family Concept Class is Easy to Be Enumerated? ICDM 2008: 482-491 - [c29]Mineichi Kudo, Atsuyoshi Nakamura, Ichigaku Takigawa:
Classification by reflective convex hulls. ICPR 2008: 1-4 - [c28]Yohji Shidara, Mineichi Kudo, Atsuyoshi Nakamura:
Classification by bagged consistent itemset rules. ICPR 2008: 1-4 - [c27]Satoshi Shirai, Mineichi Kudo, Atsuyoshi Nakamura:
Bagging, Random Subspace Method and Biding. SSPR/SPR 2008: 801-810 - 2007
- [c26]Hisashi Tosaka, Atsuyoshi Nakamura, Mineichi Kudo:
Mining Subtrees with Frequent Occurrence of Similar Subtrees. Discovery Science 2007: 286-290 - [c25]Yohji Shidara, Atsuyoshi Nakamura, Mineichi Kudo:
CCIC: Consistent Common Itemsets Classifier. MLDM 2007: 490-498 - 2006
- [c24]Atsuyoshi Nakamura:
Learning-Related Complexity of Linear Ranking Functions. ALT 2006: 378-392 - 2005
- [j8]Atsuyoshi Nakamura, Naoki Abe:
Improvements to the Linear Programming Based Scheduling of Web Advertisements. Electron. Commer. Res. 5(1): 75-98 (2005) - [j7]Atsuyoshi Nakamura:
An efficient query learning algorithm for ordered binary decision diagrams. Inf. Comput. 201(2): 178-198 (2005) - [j6]Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans Ulrich Simon:
Inner Product Spaces for Bayesian Networks. J. Mach. Learn. Res. 6: 1383-1403 (2005) - [c23]Mineichi Kudo, Atsuyoshi Nakamura:
Specific-Purpose Web Searches on the Basis of Structure and Contents. Federation over the Web 2005: 79-96 - [c22]Hidehiko Ino, Mineichi Kudo, Atsuyoshi Nakamura:
A Comparative Study of Algorithms for Finding Web Communities. ICDE Workshops 2005: 1257 - [c21]Hiroyuki Hasegawa, Mineichi Kudo, Atsuyoshi Nakamura:
Empirical Study on Usefulness of Algorithm SACwRApper for Reputation Extraction from the WWW. KES (4) 2005: 668-674 - [c20]Ichigaku Takigawa, Mineichi Kudo, Atsuyoshi Nakamura:
The Convex Subclass Method: Combinatorial Classifier Based on a Family of Convex Sets. MLDM 2005: 90-99 - [c19]Atsuyoshi Nakamura, Mineichi Kudo:
Mining Frequent Trees with Node-Inclusion Constraints. PAKDD 2005: 850-860 - [c18]Hidehiko Ino, Mineichi Kudo, Atsuyoshi Nakamura:
Partitioning of Web graphs by community topology. WWW 2005: 661-669 - [p1]Yohji Shidara, Mineichi Kudo, Atsuyoshi Nakamura:
Extraction of Generalized Rules with Automated Attribute Abstraction. Foundations of Data Mining and knowledge Discovery 2005: 161-170 - 2004
- [c17]Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt, Hans Ulrich Simon:
Bayesian Networks and Inner Product Spaces. COLT 2004: 518-533 - [c16]Ichigaku Takigawa, Mineichi Kudo, Atsuyoshi Nakamura, Jun Toyama:
On the Minimum l1-Norm Signal Recovery in Underdetermined Source Separation. ICA 2004: 193-200 - 2003
- [c15]Atsuyoshi Nakamura, Mineichi Kudo, Akira Tanaka, Kazuhiko Tanabe:
Collaborative Filtering Using Projective Restoration Operators. Discovery Science 2003: 393-401 - [c14]Atsuyoshi Nakamura, Mineichi Kudo, Akira Tanaka:
Collaborative Filtering Using Restoration Operators. PKDD 2003: 339-349 - 2002
- [j5]Atsuyoshi Nakamura, Naoki Abe:
Online Learning of Binary and n-ary Relations over Clustered Domains. J. Comput. Syst. Sci. 65(2): 224-256 (2002) - [c13]Atsuyoshi Nakamura:
Improvements in practical aspects of optimally scheduling web advertising. WWW 2002: 536-541 - 2000
- [j4]Atsuyoshi Nakamura:
Query learning of bounded-width OBDDs. Theor. Comput. Sci. 241(1-2): 83-114 (2000) - [c12]Atsuyoshi Nakamura, Naoki Abe, Hiroshi Matoba, Katsuhiro Ochiai:
Automatic recording agent for digital video server. ACM Multimedia 2000: 57-66
1990 – 1999
- 1999
- [j3]Marc Langheinrich, Atsuyoshi Nakamura, Naoki Abe, Tomonari Kamba, Yoshiyuki Koseki:
Unintrusive Customization Techniques for Web Advertising. Comput. Networks 31(11-16): 1259-1272 (1999) - [c11]Atsuyoshi Nakamura:
Learning Specialist Decision Lists. COLT 1999: 215-225 - [c10]Naoki Abe, Atsuyoshi Nakamura:
Learning to Optimally Schedule Internet Banner Advertisements. ICML 1999: 12-21 - 1998
- [j2]Atsuyoshi Nakamura, Jun'ichi Takeuchi, Naoki Abe:
Efficient Distribution-Free Population Learning of Simple Concepts. Ann. Math. Artif. Intell. 23(1-2): 53-82 (1998) - [c9]Naoki Abe, Hiroshi Mamitsuka, Atsuyoshi Nakamura:
Empirical Comparison of Competing Query Learning Methods. Discovery Science 1998: 387-388 - [c8]Atsuyoshi Nakamura, Naoki Abe:
Collaborative Filtering Using Weighted Majority Prediction Algorithms. ICML 1998: 395-403 - 1997
- [c7]Atsuyoshi Nakamura:
An Efficient Exact Learning Algorithm for Ordered Binary Decision Diagrams. ALT 1997: 307-322 - 1996
- [c6]Atsuyoshi Nakamura:
Query Learning of Bounded-Width OBDDs. ALT 1996: 37-50 - 1995
- [j1]Atsuyoshi Nakamura, Naoki Abe:
Exact Learning of Linear Combinations of Monotone Terms from Function Value Queries. Theor. Comput. Sci. 137(1): 159-176 (1995) - [c5]Atsuyoshi Nakamura, Shinji Miura:
Learning Sparse Linear Combinations of Basis Functions over a Finite Domain. ALT 1995: 138-150 - [c4]Atsuyoshi Nakamura, Naoki Abe:
On-line Learning of Binary and n-ary Relations over Multi-dimensional Clusters. COLT 1995: 214-221 - [c3]Naoki Abe, Hang Li, Atsuyoshi Nakamura:
On-line Learning of Binary Lexical Relations Using Two-dimensional Weighted Majority Algorithms. ICML 1995: 3-11 - [i1]Naoki Abe, Hang Li, Atsuyoshi Nakamura:
On-line Learning of Binary Lexical Relations Using Two-dimensional Weighted Majority Algorithms. CoRR abs/cmp-lg/9507010 (1995) - 1994
- [c2]Atsuyoshi Nakamura, Naoki Abe, Jun'ichi Takeuchi:
Efficient Distribution-free Population Learning of Simple Concepts. AII/ALT 1994: 500-515 - 1993
- [c1]Atsuyoshi Nakamura, Naoki Abe:
Exact Learning of Linear Combinations of Monotone Terms from Function Value Queries. ALT 1993: 300-313
Coauthor Index
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