Atsutoshi Kumagai

76 papers A* 25A 7B 6C 1Misc 1Journal 30Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	B	`conf`	Accelerating Graph Construction for MIPS without Search Accuracy Loss. EDBT Yasuhiro Fujiwara, Ángel López García-Arias, Yu Mitsuzumi, Yasutoshi Ida, Atsutoshi Kumagai, Masahiro Nakano, Makoto Nakatsuji, Akisato Kimura
2026	—	`conf`	Fast Vector Quantization Algorithm for ScaNN. KDD (1) Yasuhiro Fujiwara, Ángel López García-Arias, Yasutoshi Ida, Atsutoshi Kumagai, Masahiro Nakano, Makoto Nakatsuji, Akisato Kimura
2026	J	`jnl`	Meta-learning representations for learning from multiple annotators. Neurocomputing Atsutoshi Kumagai, Tomoharu Iwata, Taishi Nishiyama, Yasutoshi Ida, Yasuhiro Fujiwara
2025	A*	`conf`	Analysis of Linear Mode Connectivity via Permutation-Based Weight Matching: With Insights into Other Permutation Search Methods. ICLR Akira Ito, Masanori Yamada, Atsutoshi Kumagai
2025	J	`jnl`	Do We Really Need Permutations? Impact of Width Expansion on Linear Mode Connectivity. CoRR Akira Ito, Masanori Yamada, Daiki Chijiwa, Atsutoshi Kumagai
2025	—	`conf`	Fast Proximal Gradient Methods with Node Pruning for Tree-Structured Sparse Regularization. ECML/PKDD (5) Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
2025	A	`conf`	Importance-weighted Positive-unlabeled Learning for Distribution Shift Adaptation. AISTATS Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi, Taishi Nishiyama, Yasuhiro Fujiwara
2025	A*	`conf`	Linear Mode Connectivity between Multiple Models modulo Permutation Symmetries. ICML Akira Ito, Masanori Yamada, Atsutoshi Kumagai
2025	J	`jnl`	Meta-learning Representations for Learning from Multiple Annotators. CoRR Atsutoshi Kumagai, Tomoharu Iwata, Taishi Nishiyama, Yasutoshi Ida, Yasuhiro Fujiwara
2025	A	`conf`	Meta-learning Task-specific Regularization Weights for Few-shot Linear Regression. AISTATS Tomoharu Iwata, Atsutoshi Kumagai, Yasutoshi Ida
2025	A	`conf`	Meta-learning from Heterogeneous Tensors for Few-shot Tensor Completion. AISTATS Tomoharu Iwata, Atsutoshi Kumagai
2025	A*	`conf`	Positive-Unlabeled Diffusion Models for Preventing Sensitive Data Generation. ICLR Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai, Yuuki Yamanaka, Tomoya Yamashita
2025	J	`jnl`	Positive-Unlabeled Diffusion Models for Preventing Sensitive Data Generation. CoRR Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai, Yuuki Yamanaka, Tomoya Yamashita
2025	A*	`conf`	Positive-unlabeled AUC Maximization under Covariate Shift. ICML Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi, Taishi Nishiyama, Kazuki Adachi, Yasuhiro Fujiwara
2025	A*	`conf`	Test-time Adaptation for Regression by Subspace Alignment. ICLR Kazuki Adachi, Shin'ya Yamaguchi, Atsutoshi Kumagai, Tomoki Hamagami
2025	J	`jnl`	Transfer learning with pre-trained conditional generative models. Mach. Learn. Shin'ya Yamaguchi, Sekitoshi Kanai, Atsutoshi Kumagai, Daiki Chijiwa, Hisashi Kashima
2024	A*	`conf`	AUC Maximization under Positive Distribution Shift. NeurIPS Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi, Taishi Nishiyama, Yasuhiro Fujiwara
2024	J	`jnl`	Analysis of Linear Mode Connectivity via Permutation-Based Weight Matching. CoRR Akira Ito, Masanori Yamada, Atsutoshi Kumagai
2024	J	`jnl`	Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data. CoRR Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai, Yuki Yamanaka
2024	J	`jnl`	Efficient Algorithm for K-Multiple-Means. Proc. ACM Manag. Data Yasuhiro Fujiwara, Atsutoshi Kumagai, Yasutoshi Ida, Masahiro Nakano, Makoto Nakatsuji, Akisato Kimura
2024	A*	`conf`	Fast Iterative Hard Thresholding Methods with Pruning Gradient Computations. NeurIPS Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
2024	—	`conf`	Malicious Log Detection Using Machine Learning to Maximize the Partial AUC. CCNC Taishi Nishiyama, Atsutoshi Kumagai, Akinori Fujino, Kazunori Kamiya
2024	J	`jnl`	Meta-learning for Positive-unlabeled Classification. CoRR Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
2024	J	`jnl`	Meta-learning to calibrate Gaussian processes with deep kernels for regression uncertainty estimation. Neurocomputing Tomoharu Iwata, Atsutoshi Kumagai
2024	B	`conf`	Partial AUC Maximization for Security Log Analysis Robust to Overfitting and Noisy Labels. GLOBECOM Taishi Nishiyama, Atsutoshi Kumagai, Akinori Fujino, Kazunori Kamiya
2024	J	`jnl`	Test-time Adaptation for Regression by Subspace Alignment. CoRR Kazuki Adachi, Shin'ya Yamaguchi, Atsutoshi Kumagai, Tomoki Hamagami
2024	A*	`conf`	Zero-Shot Task Adaptation with Relevant Feature Information. AAAI Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
2023	B	`conf`	Covariance-Aware Feature Alignment with Pre-Computed Source Statistics for Test-Time Adaptation to Multiple Image Corruptions. ICIP Kazuki Adachi, Shin'ya Yamaguchi, Atsutoshi Kumagai
2023	—	`conf`	Efficient Network Representation Learning via Cluster Similarity. DASFAA (3) Yasuhiro Fujiwara, Yasutoshi Ida, Atsutoshi Kumagai, Masahiro Nakano, Akisato Kimura, Naonori Ueda
2023	J	`jnl`	Efficient Network Representation Learning via Cluster Similarity. Data Sci. Eng. Yasuhiro Fujiwara, Yasutoshi Ida, Atsutoshi Kumagai, Masahiro Nakano, Akisato Kimura, Naonori Ueda
2023	A	`conf`	Fast Block Coordinate Descent for Non-Convex Group Regularizations. AISTATS Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai
2023	A*	`conf`	Fast Regularized Discrete Optimal Transport with Group-Sparse Regularizers. AAAI Yasutoshi Ida, Sekitoshi Kanai, Kazuki Adachi, Atsutoshi Kumagai, Yasuhiro Fujiwara
2023	J	`jnl`	Fast Regularized Discrete Optimal Transport with Group-Sparse Regularizers. CoRR Yasutoshi Ida, Sekitoshi Kanai, Kazuki Adachi, Atsutoshi Kumagai, Yasuhiro Fujiwara
2023	A	`conf`	Meta-learning for Robust Anomaly Detection. AISTATS Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi, Yasuhiro Fujiwara
2023	J	`jnl`	Meta-learning of semi-supervised learning from tasks with heterogeneous attribute spaces. CoRR Tomoharu Iwata, Atsutoshi Kumagai
2023	J	`jnl`	Meta-learning to Calibrate Gaussian Processes with Deep Kernels for Regression Uncertainty Estimation. CoRR Tomoharu Iwata, Atsutoshi Kumagai
2023	A*	`conf`	Regularizing Neural Networks with Meta-Learning Generative Models. NeurIPS Shin'ya Yamaguchi, Daiki Chijiwa, Sekitoshi Kanai, Atsutoshi Kumagai, Hisashi Kashima
2023	J	`jnl`	Regularizing Neural Networks with Meta-Learning Generative Models. CoRR Shin'ya Yamaguchi, Daiki Chijiwa, Sekitoshi Kanai, Atsutoshi Kumagai, Hisashi Kashima
2022	J	`jnl`	Covariance-aware Feature Alignment with Pre-computed Source Statistics for Test-time Adaptation. CoRR Kazuki Adachi, Shin'ya Yamaguchi, Atsutoshi Kumagai
2022	B	`conf`	Fast Binary Network Hashing via Graph Clustering. IEEE Big Data Yasuhiro Fujiwara, Masahiro Nakano, Atsutoshi Kumagai, Yasutoshi Ida, Akisato Kimura, Naonori Ueda
2022	A*	`conf`	Few-shot Learning for Feature Selection with Hilbert-Schmidt Independence Criterion. NeurIPS Atsutoshi Kumagai, Tomoharu Iwata, Yasutoshi Ida, Yasuhiro Fujiwara
2022	A*	`conf`	Learning Optimal Priors for Task-Invariant Representations in Variational Autoencoders. KDD Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai, Sekitoshi Kanai, Masanori Yamada, Yuki Yamanaka, Hisashi Kashima
2022	J	`jnl`	Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space. CoRR Tomoharu Iwata, Atsutoshi Kumagai
2022	A*	`conf`	Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks. NeurIPS Daiki Chijiwa, Shin'ya Yamaguchi, Atsutoshi Kumagai, Yasutoshi Ida
2022	J	`jnl`	Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks. CoRR Daiki Chijiwa, Shin'ya Yamaguchi, Atsutoshi Kumagai, Yasutoshi Ida
2022	A*	`conf`	Sharing Knowledge for Meta-learning with Feature Descriptions. NeurIPS Tomoharu Iwata, Atsutoshi Kumagai
2022	B	`conf`	Transfer Anomaly Detection for Maximizing the Partial AUC. IJCNN Atsutoshi Kumagai, Tomoharu Iwata, Taishi Nishiyama, Yasuhiro Fujiwara
2022	J	`jnl`	Transfer Learning with Pre-trained Conditional Generative Models. CoRR Shin'ya Yamaguchi, Sekitoshi Kanai, Atsutoshi Kumagai, Daiki Chijiwa, Hisashi Kashima
2021	J	`jnl`	Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression. CoRR Masanori Yamada, Sekitoshi Kanai, Tomoharu Iwata, Tomokatsu Takahashi, Yuki Yamanaka, Hiroshi Takahashi, Atsutoshi Kumagai
2021	J	`jnl`	Fast Algorithm for Anchor Graph Hashing. Proc. VLDB Endow. Yasuhiro Fujiwara, Sekitoshi Kanai, Yasutoshi Ida, Atsutoshi Kumagai, Naonori Ueda
2021	A*	`conf`	Fast Similarity Computation for t-SNE. ICDE Yasuhiro Fujiwara, Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai, Naonori Ueda
2021	A	`conf`	Fast and Accurate Anchor Graph-based Label Prediction. CIKM Yasuhiro Fujiwara, Yasutoshi Ida, Atsutoshi Kumagai, Sekitoshi Kanai, Naonori Ueda
2021	J	`jnl`	Few-shot Learning for Unsupervised Feature Selection. CoRR Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
2021	A*	`conf`	Meta-Learning for Relative Density-Ratio Estimation. NeurIPS Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
2021	J	`jnl`	Meta-Learning for Relative Density-Ratio Estimation. CoRR Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
2021	J	`jnl`	Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection. CoRR Tomoharu Iwata, Atsutoshi Kumagai
2021	—	`conf`	Recurrent Neural Networks for Learning Long-term Temporal Dependencies with Reanalysis of Time Scale Representation. ICBK Kentaro Ohno, Atsutoshi Kumagai
2021	J	`jnl`	Recurrent Neural Networks for Learning Long-term Temporal Dependencies with Reanalysis of Time Scale Representation. CoRR Kentaro Ohno, Atsutoshi Kumagai
2021	B	`conf`	Semi-supervised Anomaly Detection on Attributed Graphs. IJCNN Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
2021	Misc	`conf`	Sharpshooting Most Beneficial Part of AUC for Detecting Malicious Logs. AusDM Taishi Nishiyama, Atsutoshi Kumagai, Akinori Fujino, Kazunori Kamiya
2020	A*	`conf`	Efficient Algorithm for the b-Matching Graph. KDD Yasuhiro Fujiwara, Atsutoshi Kumagai, Sekitoshi Kanai, Yasutoshi Ida, Naonori Ueda
2020	J	`jnl`	Few-shot Learning for Time-series Forecasting. CoRR Tomoharu Iwata, Atsutoshi Kumagai
2020	A*	`conf`	Meta-learning from Tasks with Heterogeneous Attribute Spaces. NeurIPS Tomoharu Iwata, Atsutoshi Kumagai
2020	C	`conf`	SILU: Strategy Involving Large-scale Unlabeled Logs for Improving Malware Detector. ISCC Taishi Nishiyama, Atsutoshi Kumagai, Kazunori Kamiya, Kenji Takahashi
2020	J	`jnl`	Semi-supervised Anomaly Detection on Attributed Graphs. CoRR Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
2020	J	`jnl`	Transfer Metric Learning for Unseen Domains. Data Sci. Eng. Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
2019	—	`conf`	Alchemy: Stochastic Feature Regeneration for Malicious Network Traffic Classification. COMPSAC (1) Bo Hu, Atsutoshi Kumagai, Kazunori Kamiya, Kenji Takahashi, Daniel Dalek, Ola Söderström, Kazuya Okada, Yuji Sekiya, Akihiro Nakao
2019	A	`conf`	Fast Random Forest Algorithm via Incremental Upper Bound. CIKM Yasuhiro Fujiwara, Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai, Junya Arai, Naonori Ueda
2019	A*	`conf`	Transfer Anomaly Detection by Inferring Latent Domain Representations. NeurIPS Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
2019	A*	`conf`	Transfer Metric Learning for Unseen Domains. ICDM Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
2019	A*	`conf`	Unsupervised Domain Adaptation by Matching Distributions Based on the Maximum Mean Discrepancy via Unilateral Transformations. AAAI Atsutoshi Kumagai, Tomoharu Iwata
2018	A*	`conf`	Learning Dynamics of Decision Boundaries without Additional Labeled Data. KDD Atsutoshi Kumagai, Tomoharu Iwata
2018	J	`jnl`	Zero-shot Domain Adaptation without Domain Semantic Descriptors. CoRR Atsutoshi Kumagai, Tomoharu Iwata
2017	A*	`conf`	Learning Latest Classifiers without Additional Labeled Data. IJCAI Atsutoshi Kumagai, Tomoharu Iwata
2017	A*	`conf`	Learning Non-Linear Dynamics of Decision Boundaries for Maintaining Classification Performance. AAAI Atsutoshi Kumagai, Tomoharu Iwata
2016	A*	`conf`	Learning Future Classifiers without Additional Data. AAAI Atsutoshi Kumagai, Tomoharu Iwata