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2020 – today
- 2024
[j39]Shonosuke Harada, Hisashi Kashima:
InfoCEVAE: treatment effect estimation with hidden confounding variables matching. Mach. Learn. 113(4): 1799-1817 (2024)- [j38]Guoxi Zhang
, Hisashi Kashima:
Learning state importance for preference-based reinforcement learning. Mach. Learn. 113(4): 1885-1901 (2024) - [c154]Xiaotian Lu, Jiyi Li, Zhen Wan, Xiaofeng Lin, Koh Takeuchi, Hisashi Kashima:
Evaluating Saliency Explanations in NLP by Crowdsourcing. LREC/COLING 2024: 6431-6443 - [c153]Yu Mitsuzumi, Akisato Kimura, Hisashi Kashima:
Understanding and Improving Source-Free Domain Adaptation from a Theoretical Perspective. CVPR 2024: 28515-28524 - [c152]Xiaofeng Lin, Guoxi Zhang, Xiaotian Lu, Hisashi Kashima:
Treatment Effect Estimation Under Unknown Interference. PAKDD (2) 2024: 28-42 - [c151]Yuki Wakai, Koh Takeuchi, Hisashi Kashima:
Recovering Population Dynamics from a Single Point Cloud Snapshot. PAKDD (3) 2024: 302-315 - [i45]Guoxi Zhang, Han Bao, Hisashi Kashima:
Online Policy Learning from Offline Preferences. CoRR abs/2403.10160 (2024) - [i44]Ryota Maruo, Hisashi Kashima:
Efficient Preference Elicitation in Iterative Combinatorial Auctions with Many Participants. CoRR abs/2403.19075 (2024) - [i43]Xiaotian Lu, Jiyi Li, Zhen Wan, Xiaofeng Lin, Koh Takeuchi, Hisashi Kashima:
Evaluating Saliency Explanations in NLP by Crowdsourcing. CoRR abs/2405.10767 (2024) - [i42]Shun Ito, Hisashi Kashima:
Mitigating Cognitive Biases in Multi-Criteria Crowd Assessment. CoRR abs/2407.18938 (2024) - 2023
- [j37]Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino, Hisashi Kashima:
Making individually fair predictions with causal pathways. Data Min. Knowl. Discov. 37(4): 1327-1373 (2023) - [c150]Guoxi Zhang, Hisashi Kashima:
Behavior Estimation from Multi-Source Data for Offline Reinforcement Learning. AAAI 2023: 11201-11209 - [c149]Ryosuke Ueda, Koh Takeuchi, Hisashi Kashima:
Mitigating Voter Attribute Bias for Fair Opinion Aggregation. AIES 2023: 170-180 - [c148]Akihiro Yamaguchi, Ken Ueno, Hisashi Kashima:
Time-series Shapelets with Learnable Lengths. CIKM 2023: 2866-2876 - [c147]Kosuke Yoshimura, Hisashi Kashima:
Label Selection Approach to Learning from Crowds. ICONIP (8) 2023: 207-221 - [c146]Koh Takeuchi, Ryo Nishida, Hisashi Kashima, Masaki Onishi:
Causal Effect Estimation on Hierarchical Spatial Graph Data. KDD 2023: 2145-2154 - [c145]Ryu Shirakami, Toshiya Kitahara, Koh Takeuchi, Hisashi Kashima:
QTNet: Theory-based Queue Length Prediction for Urban Traffic. KDD 2023: 4832-4841 - [c144]Shin'ya Yamaguchi, Daiki Chijiwa, Sekitoshi Kanai, Atsutoshi Kumagai, Hisashi Kashima:
Regularizing Neural Networks with Meta-Learning Generative Models. NeurIPS 2023 - [c143]Xiaofeng Lin, Guoxi Zhang, Xiaotian Lu, Han Bao, Koh Takeuchi, Hisashi Kashima:
Estimating Treatment Effects Under Heterogeneous Interference. ECML/PKDD (1) 2023: 576-592 - [c142]Xiaotian Lu, Jiyi Li, Koh Takeuchi, Hisashi Kashima:
Multiview Representation Learning from Crowdsourced Triplet Comparisons. WWW 2023: 3827-3836 - [e7]Hisashi Kashima, Tsuyoshi Idé, Wen-Chih Peng:
Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25-28, 2023, Proceedings, Part I. Lecture Notes in Computer Science 13935, Springer 2023, ISBN 978-3-031-33373-6 [contents] - [e6]Hisashi Kashima, Tsuyoshi Idé, Wen-Chih Peng:
Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25-28, 2023, Proceedings, Part II. Lecture Notes in Computer Science 13936, Springer 2023, ISBN 978-3-031-33376-7 [contents] - [e5]Hisashi Kashima, Tsuyoshi Idé, Wen-Chih Peng:
Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25-28, 2023, Proceedings, Part III. Lecture Notes in Computer Science 13937, Springer 2023, ISBN 978-3-031-33379-8 [contents] - [e4]Hisashi Kashima, Tsuyoshi Idé, Wen-Chih Peng:
Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25-28, 2023, Proceedings, Part IV. Lecture Notes in Computer Science 13938, Springer 2023, ISBN 978-3-031-33382-8 [contents] - [i41]Xiaotian Lu, Jiyi Li, Koh Takeuchi, Hisashi Kashima:
Multiview Representation Learning from Crowdsourced Triplet Comparisons. CoRR abs/2302.03987 (2023) - [i40]Ryosuke Ueda, Koh Takeuchi, Hisashi Kashima:
Mitigating Observation Biases in Crowdsourced Label Aggregation. CoRR abs/2302.13100 (2023) - [i39]Ryosuke Ueda, Koh Takeuchi, Hisashi Kashima:
Mitigating Voter Attribute Bias for Fair Opinion Aggregation. CoRR abs/2307.10749 (2023) - [i38]Shin'ya Yamaguchi, Daiki Chijiwa, Sekitoshi Kanai, Atsutoshi Kumagai, Hisashi Kashima:
Regularizing Neural Networks with Meta-Learning Generative Models. CoRR abs/2307.13899 (2023) - [i37]Kosuke Yoshimura, Hisashi Kashima:
Label Selection Approach to Learning from Crowds. CoRR abs/2308.10396 (2023) - [i36]Jill-Jênn Vie, Hisashi Kashima:
Deep Knowledge Tracing is an implicit dynamic multidimensional item response theory model. CoRR abs/2309.12334 (2023) - [i35]Xiaofeng Lin, Guoxi Zhang, Xiaotian Lu, Han Bao, Koh Takeuchi, Hisashi Kashima:
Estimating Treatment Effects Under Heterogeneous Interference. CoRR abs/2309.13884 (2023) - 2022
- [j36]Akira Tanimoto, So Yamada, Takashi Takenouchi, Masashi Sugiyama, Hisashi Kashima:
Improving imbalanced classification using near-miss instances. Expert Syst. Appl. 201: 117130 (2022) - [j35]Ryoma Sato, Makoto Yamada, Hisashi Kashima:
Poincare: Recommending Publication Venues via Treatment Effect Estimation. J. Informetrics 16(2): 101283 (2022) - [j34]Shogo Hayashi, Junya Honda, Hisashi Kashima:
Bayesian optimization with partially specified queries. Mach. Learn. 111(3): 1019-1048 (2022) - [j33]Maya Okawa, Tomoharu Iwata, Yusuke Tanaka, Takeshi Kurashima, Hiroyuki Toda, Hisashi Kashima:
Context-aware spatio-temporal event prediction via convolutional Hawkes processes. Mach. Learn. 111(8): 2929-2950 (2022) - [j32]Ryoma Sato, Makoto Yamada, Hisashi Kashima:
Constant Time Graph Neural Networks. ACM Trans. Knowl. Discov. Data 16(5): 92:1-92:31 (2022) - [c141]Sein Minn, Jill-Jênn Vie, Koh Takeuchi, Hisashi Kashima, Feida Zhu:
Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations. AAAI 2022: 12810-12818 - [c140]Jill-Jênn Vie, Tomas Rigaux, Hisashi Kashima:
Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender Systems. IEEE Big Data 2022: 5607-5614 - [c139]Ryoma Sato, Makoto Yamada, Hisashi Kashima:
Twin Papers: A Simple Framework of Causal Inference for Citations via Coupling. CIKM 2022: 4444-4448 - [c138]Guoxi Zhang, Jiyi Li, Hisashi Kashima:
Improving Pairwise Rank Aggregation via Querying for Rank Difference. DSAA 2022: 1-9 - [c137]Akihiro Yamaguchi, Ken Ueno, Hisashi Kashima:
Learning Evolvable Time-series Shapelets. ICDE 2022: 793-805 - [c136]Ryoma Sato, Makoto Yamada, Hisashi Kashima:
Re-evaluating Word Mover's Distance. ICML 2022: 19231-19249 - [c135]Ryosuke Ueda, Koh Takeuchi, Hisashi Kashima:
Mitigating Observation Biases in Crowdsourced Label Aggregation. ICPR 2022: 1171-1177 - [c134]Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai, Sekitoshi Kanai, Masanori Yamada, Yuki Yamanaka, Hisashi Kashima:
Learning Optimal Priors for Task-Invariant Representations in Variational Autoencoders. KDD 2022: 1739-1748 - [c133]Guoxi Zhang, Hisashi Kashima:
Batch Reinforcement Learning from Crowds. ECML/PKDD (4) 2022: 38-51 - [c132]Akihiro Yamaguchi, Ken Ueno, Hisashi Kashima:
Learning Time-series Shapelets Enhancing Discriminability. SDM 2022: 190-198 - [c131]Yoichi Chikahara, Makoto Yamada, Hisashi Kashima:
Feature selection for discovering distributional treatment effect modifiers. UAI 2022: 400-410 - [e3]Yasufumi Takama, Naohiro Matsumura, Katsutoshi Yada, Mitsunori Matsushita, Daisuke Katagami, Akinori Abe, Hisashi Kashima, Toshihiro Hiraoka, Takahiro Uchiya, Rafal Rzepka:
Advances in Artificial Intelligence - Selected Papers from the Annual Conference of Japanese Society of Artificial Intelligence, JSAI 2021, Virtual Event, Japan, 8-11 June 2021. Advances in Intelligent Systems and Computing 1423, Springer 2022, ISBN 978-3-030-96450-4 [contents] - [i34]Shin'ya Yamaguchi, Sekitoshi Kanai, Atsutoshi Kumagai, Daiki Chijiwa, Hisashi Kashima:
Transfer Learning with Pre-trained Conditional Generative Models. CoRR abs/2204.12833 (2022) - [i33]Yoichi Chikahara, Makoto Yamada, Hisashi Kashima:
Feature Selection for Discovering Distributional Treatment Effect Modifiers. CoRR abs/2206.00516 (2022) - [i32]Ryoma Sato, Makoto Yamada, Hisashi Kashima:
Twin Papers: A Simple Framework of Causal Inference for Citations via Coupling. CoRR abs/2208.09862 (2022) - [i31]Hisashi Kashima, Satoshi Oyama, Hiromi Arai, Junichiro Mori:
Trustworthy Human Computation: A Survey. CoRR abs/2210.12324 (2022) - [i30]Guoxi Zhang, Hisashi Kashima:
Behavior Estimation from Multi-Source Data for Offline Reinforcement Learning. CoRR abs/2211.16078 (2022) - [i29]Jill-Jênn Vie, Tomas Rigaux, Hisashi Kashima:
Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender Systems. CoRR abs/2212.09920 (2022) - 2021
- [j31]Jiuding Duan, Hisashi Kashima:
Learning to Rank for Multi-Step Ahead Time-Series Forecasting. IEEE Access 9: 49372-49386 (2021) - [c130]Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino, Hisashi Kashima:
Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint. AISTATS 2021: 145-153 - [c129]Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima:
Regret Minimization for Causal Inference on Large Treatment Space. AISTATS 2021: 946-954 - [c128]Koh Takeuchi, Ryo Nishida, Hisashi Kashima, Masaki Onishi:
Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference. AAMAS 2021: 1290-1298 - [c127]Toshihiro Kamishima, Shotaro Akaho, Yukino Baba, Hisashi Kashima:
Preliminary Experiments to Examine the Stability of Bias-Aware Techniques. BIAS 2021: 25-35 - [c126]Shonosuke Harada, Hisashi Kashima:
GraphITE: Estimating Individual Effects of Graph-structured Treatments. CIKM 2021: 659-668 - [c125]Ayato Toyokuni, Sho Yokoi, Hisashi Kashima, Makoto Yamada:
Computationally Efficient Wasserstein Loss for Structured Labels. EACL (Student Research Workshop) 2021: 1-7 - [c124]Jiyi Li, Lucas Ryo Endo, Hisashi Kashima:
Label Aggregation for Crowdsourced Triplet Similarity Comparisons. ICONIP (6) 2021: 176-185 - [c123]Shu Nakamura, Koh Takeuchi, Hisashi Kashima, Takeshi Kishikawa, Takashi Ushio, Tomoyuki Haga, Takamitsu Sasaki:
In-Vehicle Network Attack Detection Across Vehicle Models: A Supervised-Unsupervised Hybrid Approach. ITSC 2021: 1286-1291 - [c122]Maya Okawa, Tomoharu Iwata, Yusuke Tanaka, Hiroyuki Toda, Takeshi Kurashima, Hisashi Kashima:
Dynamic Hawkes Processes for Discovering Time-evolving Communities' States behind Diffusion Processes. KDD 2021: 1276-1286 - [c121]Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima:
Causal Combinatorial Factorization Machines for Set-Wise Recommendation. PAKDD (2) 2021: 498-509 - [c120]Luu Huu Phuc, Koh Takeuchi, Seiji Okajima, Arseny Tolmachev, Tomoyoshi Takebayashi, Koji Maruhashi, Hisashi Kashima:
Inter-domain Multi-relational Link Prediction. ECML/PKDD (2) 2021: 285-301 - [c119]Xiaotian Lu, Arseny Tolmachev, Tatsuya Yamamoto, Koh Takeuchi, Seiji Okajima, Tomoyoshi Takebayashi, Koji Maruhashi, Hisashi Kashima:
Crowdsourcing Evaluation of Saliency-Based XAI Methods. ECML/PKDD (5) 2021: 431-446 - [c118]Ryoma Sato, Makoto Yamada, Hisashi Kashima:
Random Features Strengthen Graph Neural Networks. SDM 2021: 333-341 - [e2]Katsutoshi Yada, Daisuke Katagami, Yasufumi Takama, Takayuki Ito, Akinori Abe, Eri Sato-Shimokawara, Junichiro Mori, Naohiro Matsumura, Hisashi Kashima:
Advances in Artificial Intelligence - Selected Papers from the Annual Conference of Japanese Society of Artificial Intelligence, JSAI 2020, Kumamoto-ken, Japan, 9-12 June 2020. Advances in Intelligent Systems and Computing 1357, Springer 2021, ISBN 978-3-030-73112-0 [contents] - [i28]Ayato Toyokuni, Sho Yokoi, Hisashi Kashima, Makoto Yamada:
Computationally Efficient Wasserstein Loss for Structured Labels. CoRR abs/2103.00899 (2021) - [i27]Maya Okawa, Tomoharu Iwata, Yusuke Tanaka, Hiroyuki Toda, Takeshi Kurashima, Hisashi Kashima:
Dynamic Hawkes Processes for Discovering Time-evolving Communities' States behind Diffusion Processes. CoRR abs/2105.11152 (2021) - [i26]Ryoma Sato, Makoto Yamada, Hisashi Kashima:
Re-evaluating Word Mover's Distance. CoRR abs/2105.14403 (2021) - [i25]Luu Huu Phuc, Koh Takeuchi, Seiji Okajima, Arseny Tolmachev, Tomoyoshi Takebayashi, Koji Maruhashi, Hisashi Kashima:
Inter-domain Multi-relational Link Prediction. CoRR abs/2106.06171 (2021) - [i24]Xiaotian Lu, Arseny Tolmachev, Tatsuya Yamamoto, Koh Takeuchi, Seiji Okajima, Tomoyoshi Takebayashi, Koji Maruhashi, Hisashi Kashima:
Crowdsourcing Evaluation of Saliency-based XAI Methods. CoRR abs/2107.00456 (2021) - [i23]Guoxi Zhang, Hisashi Kashima:
Batch Reinforcement Learning from Crowds. CoRR abs/2111.04279 (2021) - [i22]Sein Minn, Jill-Jênn Vie, Koh Takeuchi, Hisashi Kashima, Feida Zhu:
Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations. CoRR abs/2112.11209 (2021) - 2020
- [j30]Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki, Yukino Baba, Ichigaku Takigawa, Yoshihiro Yamanishi, Hisashi Kashima:
Dual graph convolutional neural network for predicting chemical networks. BMC Bioinform. 21-S(3): 94 (2020) - [j29]Shun Ito, Yukino Baba, Tetsu Isomura, Hisashi Kashima:
Synthetic accessibility assessment using auxiliary responses. Expert Syst. Appl. 145: 113106 (2020) - [c117]Luu Huu Phuc, Koh Takeuchi, Makoto Yamada, Hisashi Kashima:
Simultaneous Link Prediction on Unaligned Networks Using Graph Embedding and Optimal Transport. DSAA 2020: 245-254 - [c116]Hitoshi Kusano, Yuji Horiguchi, Yukino Baba, Hisashi Kashima:
Stress Prediction from Head Motion. DSAA 2020: 488-495 - [c115]Tatsuya Shiraishi, Tam Le, Hisashi Kashima, Makoto Yamada:
Topological Bayesian Optimization with Persistence Diagrams. ECAI 2020: 1483-1490 - [c114]Yukino Baba, Jiyi Li, Hisashi Kashima:
CrowDEA: Multi-View Idea Prioritization with Crowds. HCOMP 2020: 23-32 - [c113]Yasutoshi Ida, Sekitoshi Kanai, Yasuhiro Fujiwara, Tomoharu Iwata, Koh Takeuchi, Hisashi Kashima:
Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance. ICML 2020: 4594-4603 - [c112]Yan Gu, Jiuding Duan, Hisashi Kashima:
An Intransitivity Model for Matchup and Pairwise Comparison. ICPR 2020: 692-698 - [c111]Jiyi Li, Yasushi Kawase, Yukino Baba, Hisashi Kashima:
Performance as a Constraint: An Improved Wisdom of Crowds Using Performance Regularization. IJCAI 2020: 1534-1541 - [c110]Ryoma Sato, Makoto Yamada, Hisashi Kashima:
Fast Unbalanced Optimal Transport on a Tree. NeurIPS 2020 - [c109]Shonosuke Harada, Hisashi Kashima:
Counterfactual Propagation for Semi-supervised Individual Treatment Effect Estimation. ECML/PKDD (1) 2020: 542-558 - [i21]Ryoma Sato, Marco Cuturi, Makoto Yamada, Hisashi Kashima:
Fast and Robust Comparison of Probability Measures in Heterogeneous Spaces. CoRR abs/2002.01615 (2020) - [i20]Ryoma Sato, Makoto Yamada, Hisashi Kashima:
Random Features Strengthen Graph Neural Networks. CoRR abs/2002.03155 (2020) - [i19]Shonosuke Harada, Hisashi Kashima:
Counterfactual Propagation for Semi-Supervised Individual Treatment Effect Estimation. CoRR abs/2005.05099 (2020) - [i18]Ryoma Sato, Makoto Yamada, Hisashi Kashima:
Fast Unbalanced Optimal Transport on Tree. CoRR abs/2006.02703 (2020) - [i17]Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima:
Regret Minimization for Causal Inference on Large Treatment Space. CoRR abs/2006.05616 (2020) - [i16]Yukino Baba, Jiyi Li, Hisashi Kashima:
CrowDEA: Multi-view Idea Prioritization with Crowds. CoRR abs/2008.02354 (2020) - [i15]Yang Liu, Hisashi Kashima:
Chemical Property Prediction Under Experimental Biases. CoRR abs/2009.08687 (2020) - [i14]Shonosuke Harada, Hisashi Kashima:
GraphITE: Estimating Individual Effects of Graph-structured Treatments. CoRR abs/2009.14061 (2020) - [i13]Ryoma Sato, Makoto Yamada, Hisashi Kashima:
Poincare: Recommending Publication Venues via Treatment Effect Estimation. CoRR abs/2010.09157 (2020)
2010 – 2019
- 2019
- [c108]Jill-Jênn Vie, Hisashi Kashima:
Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing. AAAI 2019: 750-757 - [c107]Ryoma Sato, Makoto Yamada, Hisashi Kashima:
Learning to Sample Hard Instances for Graph Algorithms. ACML 2019: 503-518 - [c106]Shogo Hayashi, Yoshinobu Kawahara, Hisashi Kashima:
Active Change-Point Detection. ACML 2019: 1017-1032 - [c105]Takeru Sunahase, Yukino Baba, Hisashi Kashima:
Probabilistic Modeling of Peer Correction and Peer Assessment. EDM 2019 - [c104]Kosuke Yoshimura, Tomoaki Iwase, Yukino Baba, Hisashi Kashima:
Interdependence Model for Multi-label Classification. ICANN (4) 2019: 55-68 - [c103]Yusuke Sakata, Yukino Baba, Hisashi Kashima:
Crownn: Human-in-the-loop Network with Crowd-generated Inputs. ICASSP 2019: 7555-7559 - [c102]Shogo Hayashi, Akira Tanimoto, Hisashi Kashima:
Long-Term Prediction of Small Time-Series Data Using Generalized Distillation. IJCNN 2019: 1-8 - [c101]Daiki Tanaka, Makoto Yamada, Hisashi Kashima, Takeshi Kishikawa, Tomoyuki Haga, Takamitsu Sasaki:
In-Vehicle Network Intrusion Detection and Explanation Using Density Ratio Estimation. ITSC 2019: 2238-2243 - [c100]Yasutoshi Ida, Yasuhiro Fujiwara, Hisashi Kashima:
Fast Sparse Group Lasso. NeurIPS 2019: 1700-1708 - [c99]Ryoma Sato, Makoto Yamada, Hisashi Kashima:
Approximation Ratios of Graph Neural Networks for Combinatorial Problems. NeurIPS 2019: 4083-4092 - [c98]Rafael Pinot, Laurent Meunier, Alexandre Araujo, Hisashi Kashima, Florian Yger, Cédric Gouy-Pailler, Jamal Atif:
Theoretical evidence for adversarial robustness through randomization. NeurIPS 2019: 11838-11848 - [c97]Shonosuke Harada, Kazuki Taniguchi, Makoto Yamada, Hisashi Kashima:
Context-Regularized Neural Collaborative Filtering for Game App Recommendation. RecSys (Late-Breaking Results) 2019: 16-20 - [c96]Daiki Tanaka, Yukino Baba, Hisashi Kashima, Yuta Okubo:
Large-scale Driver Identification Using Automobile Driving Data. SMC 2019: 3441-3446 - [i12]Ryoma Sato, Makoto Yamada, Hisashi Kashima:
Constant Time Graph Neural Networks. CoRR abs/1901.07868 (2019) - [i11]Rafael Pinot, Laurent Meunier, Alexandre Araujo, Hisashi Kashima, Florian Yger, Cédric Gouy-Pailler, Jamal Atif:
Theoretical evidence for adversarial robustness through randomization: the case of the Exponential family. CoRR abs/1902.01148 (2019) - [i10]Ryoma Sato, Makoto Yamada, Hisashi Kashima:
Learning to Find Hard Instances of Graph Problems. CoRR abs/1902.09700 (2019) - [i9]Tatsuya Shiraishi, Tam Le, Hisashi Kashima, Makoto Yamada:
Topological Bayesian Optimization with Persistence Diagrams. CoRR abs/1902.09722 (2019) - [i8]Ryoma Sato, Makoto Yamada, Hisashi Kashima:
Approximation Ratios of Graph Neural Networks for Combinatorial Problems. CoRR abs/1905.10261 (2019) - 2018
- [j28]Takuya Kuwahara, Yukino Baba, Hisashi Kashima, Takeshi Kishikawa, Jun'ichi Tsurumi, Tomoyuki Haga, Yoshihiro Ujiie, Takamitsu Sasaki, Hideki Matsushima:
Supervised and Unsupervised Intrusion Detection Based on CAN Message Frequencies for In-vehicle Network. J. Inf. Process. 26: 306-313 (2018) - [c95]Ryusuke Takahama, Yukino Baba, Nobuyuki Shimizu, Sumio Fujita, Hisashi Kashima:
AdaFlock: Adaptive Feature Discovery for Human-in-the-loop Predictive Modeling. AAAI 2018: 1619-1626 - [c94]Yukino Baba, Tomoumi Takase, Kyohei Atarashi, Satoshi Oyama, Hisashi Kashima:
Data Analysis Competition Platform for Educational Purposes: Lessons Learned and Future Challenges. AAAI 2018: 7887-7892 - [c93]Junpei Naito, Yukino Baba, Hisashi Kashima, Takenori Takaki, Takuya Funo:
Predictive Modeling of Learning Continuation in Preschool Education Using Temporal Patterns of Development Tests. AAAI 2018: 7934-7940 - [c92]