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ORCIDAtsutoshi Kumagai
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2020 – today
- 2024
[j5]Tomoharu Iwata
, Atsutoshi Kumagai:
Meta-learning to calibrate Gaussian processes with deep kernels for regression uncertainty estimation. Neurocomputing 579: 127441 (2024)- [j4]Yasuhiro Fujiwara, Atsutoshi Kumagai, Yasutoshi Ida, Masahiro Nakano, Makoto Nakatsuji, Akisato Kimura:
Efficient Algorithm for K-Multiple-Means. Proc. ACM Manag. Data 2(1): 18:1-18:26 (2024) - [c32]Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara:
Zero-Shot Task Adaptation with Relevant Feature Information. AAAI 2024: 13283-13291 - [c31]Taishi Nishiyama, Atsutoshi Kumagai, Akinori Fujino, Kazunori Kamiya:
Malicious Log Detection Using Machine Learning to Maximize the Partial AUC. CCNC 2024: 339-344 - [i19]Akira Ito, Masanori Yamada, Atsutoshi Kumagai:
Analysis of Linear Mode Connectivity via Permutation-Based Weight Matching. CoRR abs/2402.04051 (2024) - [i18]Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai, Yuki Yamanaka:
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data. CoRR abs/2405.18929 (2024) - [i17]Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara:
Meta-learning for Positive-unlabeled Classification. CoRR abs/2406.03680 (2024) - 2023
- [j3]Yasuhiro Fujiwara, Yasutoshi Ida, Atsutoshi Kumagai, Masahiro Nakano, Akisato Kimura, Naonori Ueda:
Efficient Network Representation Learning via Cluster Similarity. Data Sci. Eng. 8(3): 279-291 (2023) - [c30]Yasutoshi Ida, Sekitoshi Kanai, Kazuki Adachi, Atsutoshi Kumagai, Yasuhiro Fujiwara:
Fast Regularized Discrete Optimal Transport with Group-Sparse Regularizers. AAAI 2023: 7980-7987 - [c29]Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi, Yasuhiro Fujiwara:
Meta-learning for Robust Anomaly Detection. AISTATS 2023: 675-691 - [c28]Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai:
Fast Block Coordinate Descent for Non-Convex Group Regularizations. AISTATS 2023: 2481-2493 - [c27]Yasuhiro Fujiwara, Yasutoshi Ida, Atsutoshi Kumagai, Masahiro Nakano, Akisato Kimura, Naonori Ueda:
Efficient Network Representation Learning via Cluster Similarity. DASFAA (3) 2023: 297-307 - [c26]Kazuki Adachi, Shin'ya Yamaguchi, Atsutoshi Kumagai:
Covariance-Aware Feature Alignment with Pre-Computed Source Statistics for Test-Time Adaptation to Multiple Image Corruptions. ICIP 2023: 800-804 - [c25]Shin'ya Yamaguchi, Daiki Chijiwa, Sekitoshi Kanai, Atsutoshi Kumagai, Hisashi Kashima:
Regularizing Neural Networks with Meta-Learning Generative Models. NeurIPS 2023 - [i16]Yasutoshi Ida, Sekitoshi Kanai, Kazuki Adachi, Atsutoshi Kumagai, Yasuhiro Fujiwara:
Fast Regularized Discrete Optimal Transport with Group-Sparse Regularizers. CoRR abs/2303.07597 (2023) - [i15]Shin'ya Yamaguchi, Daiki Chijiwa, Sekitoshi Kanai, Atsutoshi Kumagai, Hisashi Kashima:
Regularizing Neural Networks with Meta-Learning Generative Models. CoRR abs/2307.13899 (2023) - [i14]Tomoharu Iwata, Atsutoshi Kumagai:
Meta-learning of semi-supervised learning from tasks with heterogeneous attribute spaces. CoRR abs/2311.05088 (2023) - [i13]Tomoharu Iwata, Atsutoshi Kumagai:
Meta-learning to Calibrate Gaussian Processes with Deep Kernels for Regression Uncertainty Estimation. CoRR abs/2312.07952 (2023) - 2022
- [c24]Yasuhiro Fujiwara, Masahiro Nakano, Atsutoshi Kumagai, Yasutoshi Ida, Akisato Kimura, Naonori Ueda:
Fast Binary Network Hashing via Graph Clustering. IEEE Big Data 2022: 381-388 - [c23]Atsutoshi Kumagai, Tomoharu Iwata, Taishi Nishiyama, Yasuhiro Fujiwara:
Transfer Anomaly Detection for Maximizing the Partial AUC. IJCNN 2022: 1-8 - [c22]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 - [c21]Daiki Chijiwa, Shin'ya Yamaguchi, Atsutoshi Kumagai, Yasutoshi Ida:
Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks. NeurIPS 2022 - [c20]Tomoharu Iwata, Atsutoshi Kumagai:
Sharing Knowledge for Meta-learning with Feature Descriptions. NeurIPS 2022 - [c19]Atsutoshi Kumagai, Tomoharu Iwata, Yasutoshi Ida, Yasuhiro Fujiwara:
Few-shot Learning for Feature Selection with Hilbert-Schmidt Independence Criterion. NeurIPS 2022 - [i12]Shin'ya Yamaguchi, Sekitoshi Kanai, Atsutoshi Kumagai, Daiki Chijiwa, Hisashi Kashima:
Transfer Learning with Pre-trained Conditional Generative Models. CoRR abs/2204.12833 (2022) - [i11]Kazuki Adachi, Shin'ya Yamaguchi, Atsutoshi Kumagai:
Covariance-aware Feature Alignment with Pre-computed Source Statistics for Test-time Adaptation. CoRR abs/2204.13263 (2022) - [i10]Daiki Chijiwa, Shin'ya Yamaguchi, Atsutoshi Kumagai, Yasutoshi Ida:
Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks. CoRR abs/2205.15619 (2022) - [i9]Tomoharu Iwata, Atsutoshi Kumagai:
Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space. CoRR abs/2206.09543 (2022) - 2021
- [j2]Yasuhiro Fujiwara, Sekitoshi Kanai, Yasutoshi Ida, Atsutoshi Kumagai, Naonori Ueda:
Fast Algorithm for Anchor Graph Hashing. Proc. VLDB Endow. 14(6): 916-928 (2021) - [c18]Taishi Nishiyama, Atsutoshi Kumagai, Akinori Fujino, Kazunori Kamiya:
Sharpshooting Most Beneficial Part of AUC for Detecting Malicious Logs. AusDM 2021: 31-46 - [c17]Yasuhiro Fujiwara, Yasutoshi Ida, Atsutoshi Kumagai, Sekitoshi Kanai, Naonori Ueda:
Fast and Accurate Anchor Graph-based Label Prediction. CIKM 2021: 504-513 - [c16]Kentaro Ohno, Atsutoshi Kumagai:
Recurrent Neural Networks for Learning Long-term Temporal Dependencies with Reanalysis of Time Scale Representation. ICBK 2021: 182-189 - [c15]Yasuhiro Fujiwara, Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai, Naonori Ueda:
Fast Similarity Computation for t-SNE. ICDE 2021: 1691-1702 - [c14]Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara:
Semi-supervised Anomaly Detection on Attributed Graphs. IJCNN 2021: 1-8 - [c13]Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara:
Meta-Learning for Relative Density-Ratio Estimation. NeurIPS 2021: 30426-30438 - [i8]Masanori Yamada, Sekitoshi Kanai, Tomoharu Iwata, Tomokatsu Takahashi, Yuki Yamanaka, Hiroshi Takahashi, Atsutoshi Kumagai:
Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression. CoRR abs/2102.02950 (2021) - [i7]Tomoharu Iwata, Atsutoshi Kumagai:
Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection. CoRR abs/2103.00684 (2021) - [i6]Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara:
Meta-Learning for Relative Density-Ratio Estimation. CoRR abs/2107.00801 (2021) - [i5]Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara:
Few-shot Learning for Unsupervised Feature Selection. CoRR abs/2107.00816 (2021) - [i4]Kentaro Ohno, Atsutoshi Kumagai:
Recurrent Neural Networks for Learning Long-term Temporal Dependencies with Reanalysis of Time Scale Representation. CoRR abs/2111.03282 (2021) - 2020
- [j1]Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara:
Transfer Metric Learning for Unseen Domains. Data Sci. Eng. 5(2): 140-151 (2020) - [c12]Taishi Nishiyama, Atsutoshi Kumagai, Kazunori Kamiya, Kenji Takahashi:
SILU: Strategy Involving Large-scale Unlabeled Logs for Improving Malware Detector. ISCC 2020: 1-7 - [c11]Yasuhiro Fujiwara, Atsutoshi Kumagai, Sekitoshi Kanai, Yasutoshi Ida, Naonori Ueda:
Efficient Algorithm for the b-Matching Graph. KDD 2020: 187-197 - [c10]Tomoharu Iwata, Atsutoshi Kumagai:
Meta-learning from Tasks with Heterogeneous Attribute Spaces. NeurIPS 2020 - [i3]Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara:
Semi-supervised Anomaly Detection on Attributed Graphs. CoRR abs/2002.12011 (2020) - [i2]Tomoharu Iwata, Atsutoshi Kumagai:
Few-shot Learning for Time-series Forecasting. CoRR abs/2009.14379 (2020)
2010 – 2019
- 2019
- [c9]Atsutoshi Kumagai, Tomoharu Iwata:
Unsupervised Domain Adaptation by Matching Distributions Based on the Maximum Mean Discrepancy via Unilateral Transformations. AAAI 2019: 4106-4113 - [c8]Yasuhiro Fujiwara, Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai, Junya Arai, Naonori Ueda:
Fast Random Forest Algorithm via Incremental Upper Bound. CIKM 2019: 2205-2208 - [c7]Bo Hu, Atsutoshi Kumagai, Kazunori Kamiya, Kenji Takahashi, Daniel Dalek, Ola Söderström, Kazuya Okada, Yuji Sekiya, Akihiro Nakao:
Alchemy: Stochastic Feature Regeneration for Malicious Network Traffic Classification. COMPSAC (1) 2019: 346-351 - [c6]Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara:
Transfer Metric Learning for Unseen Domains. ICDM 2019: 1168-1173 - [c5]Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara:
Transfer Anomaly Detection by Inferring Latent Domain Representations. NeurIPS 2019: 2467-2477 - 2018
- [c4]Atsutoshi Kumagai, Tomoharu Iwata:
Learning Dynamics of Decision Boundaries without Additional Labeled Data. KDD 2018: 1627-1636 - [i1]Atsutoshi Kumagai, Tomoharu Iwata:
Zero-shot Domain Adaptation without Domain Semantic Descriptors. CoRR abs/1807.02927 (2018) - 2017
- [c3]Atsutoshi Kumagai, Tomoharu Iwata:
Learning Non-Linear Dynamics of Decision Boundaries for Maintaining Classification Performance. AAAI 2017: 2117-2123 - [c2]Atsutoshi Kumagai, Tomoharu Iwata:
Learning Latest Classifiers without Additional Labeled Data. IJCAI 2017: 2039-2045 - 2016
- [c1]Atsutoshi Kumagai, Tomoharu Iwata:
Learning Future Classifiers without Additional Data. AAAI 2016: 1772-1778
Coauthor Index
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last updated on 2024-10-07 21:17 CEST by the dblp team
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