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2020 – today
- 2024
[j20]Yuwei Sun
, Hideya Ochiai, Jun Sakuma:
Attacking-Distance-Aware Attack: Semi-targeted Model Poisoning on Federated Learning. IEEE Trans. Artif. Intell. 5(2): 925-939 (2024)- [j19]Daiki Morinaga, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Convergence Rate of the (1+1)-ES on Locally Strongly Convex and Lipschitz Smooth Functions. IEEE Trans. Evol. Comput. 28(2): 501-515 (2024) - [c92]Kunihiro Ito, Batnyam Enkhtaivan, Isamu Teranishi, Jun Sakuma:
Trojan attribute inference attack on gradient boosting decision trees. EuroS&P 2024: 542-559 - [c91]Daiki Morinaga, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Linear Convergence Rate Analysis of the (1+1)-ES on Locally Strongly Convex and Lipschitz Smooth Functions. GECCO Companion 2024: 49-50 - [i41]Yuwei Sun, Ippei Fujisawa, Arthur Juliani, Jun Sakuma, Ryota Kanai:
Remembering Transformer for Continual Learning. CoRR abs/2404.07518 (2024) - [i40]Yu Zhe, Rei Nagaike, Daiki Nishiyama, Kazuto Fukuchi, Jun Sakuma:
Adversarial Attacks on Hidden Tasks in Multi-Task Learning. CoRR abs/2405.15244 (2024) - [i39]Mitsuhiro Fujikawa, Yohei Akimoto, Jun Sakuma, Kazuto Fukuchi:
Harnessing the Power of Vicinity-Informed Analysis for Classification under Covariate Shift. CoRR abs/2405.16906 (2024) - [i38]Shojiro Yamabe, Kazuto Fukuchi, Ryoma Senda, Jun Sakuma:
Behavior-Targeted Attack on Reinforcement Learning with Limited Access to Victim's Policy. CoRR abs/2406.03862 (2024) - [i37]Yu Zhe, Jun Sakuma:
Zero-shot domain adaptation based on dual-level mix and contrast. CoRR abs/2406.18996 (2024) - [i36]Yu Zhe, Jun Sakuma:
Parameter Matching Attack: Enhancing Practical Applicability of Availability Attacks. CoRR abs/2407.02437 (2024) - 2023
- [j18]Atsuhiro Miyagi, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Adaptive scenario subset selection for worst-case optimization and its application to well placement optimization. Appl. Soft Comput. 133: 109842 (2023) - [j17]Daiki Nishiyama, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma:
CAMRI Loss: Improving the Recall of a Specific Class without Sacrificing Accuracy. IEICE Trans. Inf. Syst. 106(4): 523-537 (2023) - [j16]Jiayang Liu, Weiming Zhang, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma:
Unauthorized AI cannot recognize me: Reversible adversarial example. Pattern Recognit. 134: 109048 (2023) - [j15]Atsuhiro Miyagi, Yoshiki Miyauchi, Atsuo Maki, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Covariance Matrix Adaptation Evolutionary Strategy with Worst-Case Ranking Approximation for Min-Max Optimization and Its Application to Berthing Control Tasks. ACM Trans. Evol. Learn. Optim. 3(2): 8:1-8:32 (2023) - [j14]Thien Q. Tran, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma:
Statistically Significant Pattern Mining With Ordinal Utility. IEEE Trans. Knowl. Data Eng. 35(9): 8770-8783 (2023) - [c90]Junki Mori, Ryo Furukawa, Isamu Teranishi, Jun Sakuma:
Heterogeneous Domain Adaptation with Positive and Unlabeled Data. IEEE Big Data 2023: 778-787 - [c89]Yoshimasa Akimoto, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma:
Privformer: Privacy-preserving Transformer with MPC. EuroS&P 2023: 392-410 - [c88]Kaiwen Xu, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma:
Statistically Significant Concept-based Explanation of Image Classifiers via Model Knockoffs. IJCAI 2023: 519-526 - [c87]Joshua Butke, Noriaki Hashimoto, Ichiro Takeuchi, Hiroaki Miyoshi, Koichi Ohshima, Jun Sakuma:
Mixing Histopathology Prototypes into Robust Slide-Level Representations for Cancer Subtyping. MLMI@MICCAI (2) 2023: 114-123 - [c86]Kazuto Fukuchi, Jun Sakuma:
Demographic Parity Constrained Minimax Optimal Regression under Linear Model. NeurIPS 2023 - [c85]Kazuya Kakizaki, Kazuto Fukuchi, Jun Sakuma:
Certified Defense for Content Based Image Retrieval. WACV 2023: 4550-4559 - [i35]Rei Sato, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement Learning. CoRR abs/2301.13343 (2023) - [i34]Atsuhiro Miyagi, Yoshiki Miyauchi, Atsuo Maki, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Covariance Matrix Adaptation Evolutionary Strategy with Worst-Case Ranking Approximation for Min-Max Optimization and its Application to Berthing Control Tasks. CoRR abs/2303.16079 (2023) - [i33]Yuwei Sun, Hideya Ochiai, Jun Sakuma:
Instance-level Trojan Attacks on Visual Question Answering via Adversarial Learning in Neuron Activation Space. CoRR abs/2304.00436 (2023) - [i32]Junki Mori, Ryo Furukawa, Isamu Teranishi, Jun Sakuma:
Heterogeneous Domain Adaptation with Positive and Unlabeled Data. CoRR abs/2304.07955 (2023) - [i31]Kaiwen Xu, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma:
Statistically Significant Concept-based Explanation of Image Classifiers via Model Knockoffs. CoRR abs/2305.18362 (2023) - [i30]Joshua Butke, Noriaki Hashimoto, Ichiro Takeuchi, Hiroaki Miyoshi, Koichi Ohshima, Jun Sakuma:
Mixing Histopathology Prototypes into Robust Slide-Level Representations for Cancer Subtyping. CoRR abs/2310.12769 (2023) - 2022
- [j13]Yu Zhe, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma:
Domain Generalization via Adversarially Learned Novel Domains. IEEE Access 10: 101855-101868 (2022) - [j12]Naoki Sakamoto, Rei Sato, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Explicitly Constrained Black-Box Optimization With Disconnected Feasible Domains Using Deep Generative Models. IEEE Access 10: 117501-117514 (2022) - [j11]Kazuto Fukuchi, Chia-Mu Yu, Jun Sakuma:
Locally Differentially Private Minimum Finding. IEICE Trans. Inf. Syst. 105-D(8): 1418-1430 (2022) - [c84]Thien Q. Tran, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma:
Unsupervised Causal Binary Concepts Discovery with VAE for Black-Box Model Explanation. AAAI 2022: 9614-9622 - [c83]Atsuhiro Miyagi, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Black-box min-max continuous optimization using CMA-ES with worst-case ranking approximation. GECCO 2022: 823-831 - [c82]Yu Zhe, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma:
Domain Generalization Via Adversarially Learned Novel Domains. ICME 2022: 1-6 - [c81]Syou Hirofumi, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma:
Did You Use My GAN to Generate Fake? Post-hoc Attribution of GAN Generated Images via Latent Recovery. IJCNN 2022: 1-8 - [c80]Daiki Nishiyama, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma:
CAMRI Loss: Improving Recall of a Specific Class without Sacrificing Accuracy. IJCNN 2022: 1-8 - [c79]Rei Sato, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement Learning. IJCNN 2022: 1-10 - [c78]Yuwei Sun, Hideya Ochiai, Jun Sakuma:
Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis. IJCNN 2022: 1-8 - [c77]Takumi Tanabe, Rei Sato, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification. NeurIPS 2022 - [i29]Yuwei Sun, Hideya Ochiai, Jun Sakuma:
Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis. CoRR abs/2203.11633 (2022) - [i28]Atsuhiro Miyagi, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Black-Box Min-Max Continuous Optimization Using CMA-ES with Worst-case Ranking Approximation. CoRR abs/2204.02646 (2022) - [i27]Daiki Nishiyama, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma:
CAMRI Loss: Improving Recall of a Specific Class without Sacrificing Accuracy. CoRR abs/2209.10920 (2022) - [i26]Daiki Morinaga, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Convergence rate of the (1+1)-evolution strategy on locally strongly convex functions with lipschitz continuous gradient and their monotonic transformations. CoRR abs/2209.12467 (2022) - [i25]Takumi Tanabe, Rei Sato, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification. CoRR abs/2211.03413 (2022) - [i24]Atsuhiro Miyagi, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Adaptive Scenario Subset Selection for Worst-Case Optimization and its Application to Well Placement Optimization. CoRR abs/2211.16574 (2022) - 2021
- [c76]Rei Sato, Jun Sakuma, Youhei Akimoto:
AdvantageNAS: Efficient Neural Architecture Search with Credit Assignment. AAAI 2021: 9489-9496 - [c75]Kazuya Kakizaki, Taiki Miyagawa, Inderjeet Singh, Jun Sakuma:
Toward Practical Adversarial Attacks on Face Verification Systems. BIOSIG 2021: 113-124 - [c74]Atsuhiro Miyagi, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Adaptive scenario subset selection for min-max black-box continuous optimization. GECCO 2021: 697-705 - [c73]Takumi Tanabe, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Level generation for angry birds with sequential VAE and latent variable evolution. GECCO 2021: 1052-1060 - [c72]Daiki Morinaga, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Convergence rate of the (1+1)-evolution strategy with success-based step-size adaptation on convex quadratic functions. GECCO 2021: 1169-1177 - [i23]Daiki Morinaga, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Convergence Rate of the (1+1)-Evolution Strategy with Success-Based Step-Size Adaptation on Convex Quadratic Functions. CoRR abs/2103.01578 (2021) - [i22]Takumi Tanabe, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Level Generation for Angry Birds with Sequential VAE and Latent Variable Evolution. CoRR abs/2104.06106 (2021) - [i21]Taiga Ono, Takeshi Sugawara, Jun Sakuma, Tatsuya Mori:
Application of Adversarial Examples to Physical ECG Signals. CoRR abs/2108.08972 (2021) - [i20]Thien Q. Tran, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma:
Unsupervised Causal Binary Concepts Discovery with VAE for Black-box Model Explanation. CoRR abs/2109.04518 (2021) - 2020
- [c71]Hiromu Yakura, Youhei Akimoto, Jun Sakuma:
Generate (Non-Software) Bugs to Fool Classifiers. AAAI 2020: 1070-1078 - [c70]Naoki Sakamoto, Eiji Semmatsu, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto:
Deep generative model for non-convex constraint handling. GECCO 2020: 636-644 - [c69]Thien Q. Tran, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma:
Statistically Significant Pattern Mining with Ordinal Utility. KDD 2020: 1645-1655 - [i19]Thien Q. Tran, Jun Sakuma:
Seasonal-adjustment Based Feature Selection Method for Large-scale Search Engine Logs. CoRR abs/2008.09727 (2020) - [i18]Thien Q. Tran, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma:
Statistically Significant Pattern Mining with Ordinal Utility. CoRR abs/2008.10747 (2020) - [i17]Rei Sato, Jun Sakuma, Youhei Akimoto:
AdvantageNAS: Efficient Neural Architecture Search with Credit Assignment. CoRR abs/2012.06138 (2020)
2010 – 2019
- 2019
- [j10]Hiromu Yakura, Shinnosuke Shinozaki, Reon Nishimura, Yoshihiro Oyama, Jun Sakuma:
Neural malware analysis with attention mechanism. Comput. Secur. 87 (2019) - [c68]Ryota Namba, Jun Sakuma:
Robust Watermarking of Neural Network with Exponential Weighting. AsiaCCS 2019: 228-240 - [c67]Hiromu Yakura, Jun Sakuma:
Robust Audio Adversarial Example for a Physical Attack. IJCAI 2019: 5334-5341 - [c66]Thien Q. Tran, Jun Sakuma:
Seasonal-adjustment Based Feature Selection Method for Predicting Epidemic with Large-scale Search Engine Logs. KDD 2019: 2857-2866 - [i16]Ryota Namba, Jun Sakuma:
Robust Watermarking of Neural Network with Exponential Weighting. CoRR abs/1901.06151 (2019) - [i15]Kazuto Fukuchi, Chia-Mu Yu, Arashi Haishima, Jun Sakuma:
Locally Differentially Private Minimum Finding. CoRR abs/1905.11067 (2019) - [i14]Hiromu Yakura, Youhei Akimoto, Jun Sakuma:
Generate (non-software) Bugs to Fool Classifiers. CoRR abs/1911.08644 (2019) - 2018
- [j9]Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, Jun Sakuma:
Model-based and actual independence for fairness-aware classification. Data Min. Knowl. Discov. 32(1): 258-286 (2018) - [j8]Hiroaki Kikuchi, Takayasu Yamaguchi, Koki Hamada, Yuji Yamaoka, Hidenobu Oguri, Jun Sakuma:
Study on Record Linkage of Anonymizied Data. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 101-A(1): 19-28 (2018) - [j7]Takao Murakami, Hideitsu Hino, Jun Sakuma:
Toward Distribution Estimation under Local Differential Privacy with Small Samples. Proc. Priv. Enhancing Technol. 2018(3): 84-104 (2018) - [j6]Jun Sakuma, Tatsuya Osame:
Recommendation with k-Anonymized Ratings. Trans. Data Priv. 11(1): 47-60 (2018) - [c65]Hiroyuki Hanada, Atsushi Shibagaki, Jun Sakuma, Ichiro Takeuchi:
Efficiently Monitoring Small Data Modification Effect for Large-Scale Learning in Changing Environment. AAAI 2018: 1314-1321 - [c64]Wenjie Lu, Jun Sakuma:
More Practical Privacy-Preserving Machine Learning as A Service via Efficient Secure Matrix Multiplication. WAHC@CCS 2018: 25-36 - [c63]Wenjie Lu, Jun-Jie Zhou, Jun Sakuma:
Non-interactive and Output Expressive Private Comparison from Homomorphic Encryption. AsiaCCS 2018: 67-74 - [c62]Hiromu Yakura, Shinnosuke Shinozaki, Reon Nishimura, Yoshihiro Oyama, Jun Sakuma:
Malware Analysis of Imaged Binary Samples by Convolutional Neural Network with Attention Mechanism. CODASPY 2018: 127-134 - [c61]Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, Jun Sakuma:
Recommendation Independence. FAT 2018: 187-201 - [c60]Kazuto Fukuchi, Jun Sakuma:
Minimax Optimal Additive Functional Estimation with Discrete Distribution: Slow Divergence Speed Case. ISIT 2018: 1041-1045 - [c59]Noboru Kunihiro, Wenjie Lu, Takashi Nishide, Jun Sakuma:
Outsourced Private Function Evaluation with Privacy Policy Enforcement. TrustCom/BigDataSE 2018: 412-423 - [i13]Kazuto Fukuchi, Jun Sakuma:
Minimax Optimal Additive Functional Estimation with Discrete Distribution: Slow Divergence Speed Case. CoRR abs/1801.05362 (2018) - [i12]Hiroyuki Hanada, Toshiyuki Takada, Jun Sakuma, Ichiro Takeuchi:
Interval-based Prediction Uncertainty Bound Computation in Learning with Missing Values. CoRR abs/1803.00218 (2018) - [i11]Hiromu Yakura, Jun Sakuma:
Robust Audio Adversarial Example for a Physical Attack. CoRR abs/1810.11793 (2018) - [i10]Kazuto Fukuchi, Jun Sakuma:
Minimax Optimal Additive Functional Estimation with Discrete Distribution. CoRR abs/1812.00001 (2018) - [i9]Tatsuki Koga, Naoki Nonaka, Jun Sakuma, Jun Seita:
General-to-Detailed GAN for Infrequent Class Medical Images. CoRR abs/1812.01690 (2018) - 2017
- [c58]Hiromu Yakura, Shinnosuke Shinozaki, Reon Nishimura, Yoshihiro Oyama, Jun Sakuma:
Malware Analysis of Imaged Binary Samples by Convolutional Neural Network with Attention Mechanism. AISec@CCS 2017: 55-56 - [c57]Keita Emura, Takuya Hayashi, Noboru Kunihiro, Jun Sakuma:
Mis-operation Resistant Searchable Homomorphic Encryption. AsiaCCS 2017: 215-229 - [c56]Kosuke Kusano, Ichiro Takeuchi, Jun Sakuma:
Privacy-preserving and Optimal Interval Release for Disease Susceptibility. AsiaCCS 2017: 532-545 - [c55]Takahito Kaiho, Wenjie Lu, Toshiyuki Amagasa, Jun Sakuma:
Towards Privacy-Preserving Record Linkage with Record-Wise Linkage Policy. DEXA (1) 2017: 233-248 - [c54]Kazuto Fukuchi, Quang Khai Tran, Jun Sakuma:
Differentially Private Empirical Risk Minimization with Input Perturbation. DS 2017: 82-90 - [c53]Kazuya Kakizaki, Kazuto Fukuchi, Jun Sakuma:
Differentially Private Chi-squared Test by Unit Circle Mechanism. ICML 2017: 1761-1770 - [c52]Kazuto Fukuchi, Jun Sakuma:
Minimax optimal estimators for additive scalar functionals of discrete distributions. ISIT 2017: 2103-2107 - [c51]Wenjie Lu, Shohei Kawasaki, Jun Sakuma:
Using Fully Homomorphic Encryption for Statistical Analysis of Categorical, Ordinal and Numerical Data. NDSS 2017 - [c50]Xu Long, Jun Sakuma:
Differentially Private Semi-Supervised Classification. SMARTCOMP 2017: 1-6 - [i8]Kazuto Fukuchi, Jun Sakuma:
Minimax Optimal Estimators for Additive Scalar Functionals of Discrete Distributions. CoRR abs/1701.06381 (2017) - [i7]Jun Sakuma, Tatsuya Osame:
Recommendation with k-anonymized Ratings. CoRR abs/1707.03334 (2017) - [i6]Kazuto Fukuchi, Quang Khai Tran, Jun Sakuma:
Differentially Private Empirical Risk Minimization with Input Perturbation. CoRR abs/1710.07425 (2017) - 2016
- [c49]Toshiyuki Takada, Hiroyuki Hanada, Yoshiji Yamada, Jun Sakuma, Ichiro Takeuchi:
Secure Approximation Guarantee for Cryptographically Private Empirical Risk Minimization. ACML 2016: 126-141 - [c48]Hiroaki Kikuchi, Takayasu Yamaguchi, Koki Hamada, Yuji Yamaoka, Hidenobu Oguri, Jun Sakuma:
Ice and Fire: Quantifying the Risk of Re-identification and Utility in Data Anonymization. AINA 2016: 1035-1042 - [c47]Hiroaki Kikuchi, Takayasu Yamaguchi, Koki Hamada, Yuji Yamaoka, Hidenobu Oguri, Jun Sakuma:
A Study from the Data Anonymization Competition Pwscup 2015. DPM/QASA@ESORICS 2016: 230-237 - [c46]Tadanori Teruya, Yoshiki Aoki, Jun Sakuma:
Fairy ring: Ubiquitous secure multiparty computation framework for smartphone applications. ISITA 2016: 708-712 - [i5]Toshiyuki Takada, Hiroyuki Hanada, Yoshiji Yamada, Jun Sakuma, Ichiro Takeuchi:
Secure Approximation Guarantee for Cryptographically Private Empirical Risk Minimization. CoRR abs/1602.04579 (2016) - [i4]Hiroyuki Hanada, Atsushi Shibagaki, Jun Sakuma, Ichiro Takeuchi:
Efficiently Bounding Optimal Solutions after Small Data Modification in Large-Scale Empirical Risk Minimization. CoRR abs/1606.00136 (2016) - [i3]Wenjie Lu, Shohei Kawasaki, Jun Sakuma:
Using Fully Homomorphic Encryption for Statistical Analysis of Categorical, Ordinal and Numerical Data. IACR Cryptol. ePrint Arch. 2016: 1163 (2016) - 2015
- [j5]Kana Shimizu, Koji Nuida, Hiromi Arai, Shigeo Mitsunari, Nuttapong Attrapadung, Michiaki Hamada, Koji Tsuda, Takatsugu Hirokawa, Jun Sakuma, Goichiro Hanaoka, Kiyoshi Asai:
Privacy-preserving search for chemical compound databases. BMC Bioinform. 16(S18): S6 (2015) - [j4]Kazuto Fukuchi, Toshihiro Kamishima, Jun Sakuma:
Prediction with Model-Based Neutrality. IEICE Trans. Inf. Syst. 98-D(8): 1503-1516 (2015) - [j3]Wenjie Lu, Yoshiji Yamada, Jun Sakuma:
Privacy-preserving genome-wide association studies on cloud environment using fully homomorphic encryption. BMC Medical Informatics Decis. Mak. 15-S(5): S1 (2015) - [c45]Rina Okada, Kazuto Fukuchi, Jun Sakuma:
Differentially Private Analysis of Outliers. ECML/PKDD (2) 2015: 458-473 - [c44]Wenjie Lu, Yoshiji Yamada, Jun Sakuma:
Efficient Secure Outsourcing of Genome-Wide Association Studies. IEEE Symposium on Security and Privacy Workshops 2015: 3-6 - [c43]David A. duVerle, Shohei Kawasaki, Yoshiji Yamada, Jun Sakuma, Koji Tsuda:
Privacy-Preserving Statistical Analysis by Exact Logistic Regression. IEEE Symposium on Security and Privacy Workshops 2015: 7-16 - [i2]Kazuto Fukuchi, Jun Sakuma:
Fairness-Aware Learning with Restriction of Universal Dependency using f-Divergences. CoRR abs/1506.07721 (2015) - [i1]Rina Okada, Kazuto Fukuchi, Kazuya Kakizaki, Jun Sakuma:
Differentially Private Analysis of Outliers. CoRR abs/1507.06763 (2015) - 2014
- [j2]Hiroaki Kikuchi, Jun Sakuma:
Bloom Filter Bootstrap: Privacy-Preserving Estimation of the Size of an Intersection. J. Inf. Process. 22(2): 388-400 (2014) - [c42]Hiroaki Kikuchi, Tomoki Sato, Jun Sakuma:
Privacy-Preserving Hypothesis Testing for the Analysis of Epidemiological Medical Data. AINA 2014: 359-365 - [c41]Toshiyuki Amagasa, Fan Zhang, Jun Sakuma, Hiroyuki Kitagawa:
A scheme for privacy-preserving ontology mapping. IDEAS 2014: 87-95 - [c40]Hiroaki Kikuchi, Tomoki Sato, Jun Sakuma:
Privacy-Preserving Dose-Response Relationship Test. NBiS 2014: 506-510 - [c39]Kazuto Fukuchi, Jun Sakuma:
Neutralized Empirical Risk Minimization with Generalization Neutrality Bound. ECML/PKDD (1) 2014: 418-433 - [c38]Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, Jun Sakuma:
Correcting Popularity Bias by Enhancing Recommendation Neutrality. RecSys Posters 2014 - [c37]Hirohito Sasakawa, Hiroki Harada, David duVerle, Hiroki Arimura, Koji Tsuda, Jun Sakuma:
Oblivious Evaluation of Non-deterministic Finite Automata with Application to Privacy-Preserving Virus Genome Detection. WPES 2014: 21-30 - 2013
- [c36]Hiroaki Kikuchi, Jun Sakuma:
Bloom Filter Bootstrap: Privacy-Preserving Estimation of the Size of an Intersection. DBSec 2013: 145-163 - [c35]