Learning Future Classifiers without Additional Data. - Atsutoshi Kumagai; Tomoharu Iwata (AAAI, 2016)

Learning Non-Linear Dynamics of Decision Boundaries for Maintaining Classification Performance. - Atsutoshi Kumagai; Tomoharu Iwata (AAAI, 2017)

Learning Latest Classifiers without Additional Labeled Data. - Atsutoshi Kumagai; Tomoharu Iwata (IJCAI, 2017)

Learning Dynamics of Decision Boundaries without Additional Labeled Data. - Atsutoshi Kumagai; Tomoharu Iwata (KDD, 2018)

Zero-shot Domain Adaptation without Domain Semantic Descriptors. - Atsutoshi Kumagai; Tomoharu Iwata (CoRR, 2018)

Unsupervised Domain Adaptation by Matching Distributions Based on the Maximum Mean Discrepancy via Unilateral Transformations. - Atsutoshi Kumagai; Tomoharu Iwata (AAAI, 2019)

Fast Random Forest Algorithm via Incremental Upper Bound. - Yasuhiro Fujiwara; Yasutoshi Ida; Sekitoshi Kanai; Atsutoshi Kumagai; Junya Arai; Naonori Ueda (CIKM, 2019)

Alchemy: Stochastic Feature Regeneration for Malicious Network Traffic Classification. - Bo Hu; Atsutoshi Kumagai; Kazunori Kamiya; Kenji Takahashi; Daniel Dalek; Ola Söderström; Kazuya Okada; Yuji Sekiya; Akihiro Nakao (COMPSAC (1), 2019)

Transfer Metric Learning for Unseen Domains. - Atsutoshi Kumagai; Tomoharu Iwata; Yasuhiro Fujiwara (ICDM, 2019)

Transfer Anomaly Detection by Inferring Latent Domain Representations. - Atsutoshi Kumagai; Tomoharu Iwata; Yasuhiro Fujiwara (NeurIPS, 2019)

SILU: Strategy Involving Large-scale Unlabeled Logs for Improving Malware Detector. - Taishi Nishiyama; Atsutoshi Kumagai; Kazunori Kamiya; Kenji Takahashi (ISCC, 2020)

Efficient Algorithm for the b-Matching Graph. - Yasuhiro Fujiwara; Atsutoshi Kumagai; Sekitoshi Kanai; Yasutoshi Ida; Naonori Ueda (KDD, 2020)

Meta-learning from Tasks with Heterogeneous Attribute Spaces. - Tomoharu Iwata; Atsutoshi Kumagai (NeurIPS, 2020)

Few-shot Learning for Time-series Forecasting. - Tomoharu Iwata; Atsutoshi Kumagai (CoRR, 2020)

Sharpshooting Most Beneficial Part of AUC for Detecting Malicious Logs. - Taishi Nishiyama; Atsutoshi Kumagai; Akinori Fujino; Kazunori Kamiya (AusDM, 2021)

Fast and Accurate Anchor Graph-based Label Prediction. - Yasuhiro Fujiwara; Yasutoshi Ida; Atsutoshi Kumagai; Sekitoshi Kanai; Naonori Ueda (CIKM, 2021)

Recurrent Neural Networks for Learning Long-term Temporal Dependencies with Reanalysis of Time Scale Representation. - Kentaro Ohno; Atsutoshi Kumagai (ICBK, 2021)

Fast Similarity Computation for t-SNE. - Yasuhiro Fujiwara; Yasutoshi Ida; Sekitoshi Kanai; Atsutoshi Kumagai; Naonori Ueda (ICDE, 2021)

Semi-supervised Anomaly Detection on Attributed Graphs. - Atsutoshi Kumagai; Tomoharu Iwata; Yasuhiro Fujiwara (IJCNN, 2021)

Meta-Learning for Relative Density-Ratio Estimation. - Atsutoshi Kumagai; Tomoharu Iwata; Yasuhiro Fujiwara (NeurIPS, 2021)

Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression. - Masanori Yamada; Sekitoshi Kanai; Tomoharu Iwata; Tomokatsu Takahashi; Yuki Yamanaka; Hiroshi Takahashi; Atsutoshi Kumagai (CoRR, 2021)

Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection. - Tomoharu Iwata; Atsutoshi Kumagai (CoRR, 2021)

Few-shot Learning for Unsupervised Feature Selection. - Atsutoshi Kumagai; Tomoharu Iwata; Yasuhiro Fujiwara (CoRR, 2021)

Fast Algorithm for Anchor Graph Hashing. - Yasuhiro Fujiwara; Sekitoshi Kanai; Yasutoshi Ida; Atsutoshi Kumagai; Naonori Ueda (Proc. VLDB Endow., 2021)

Fast Binary Network Hashing via Graph Clustering. - Yasuhiro Fujiwara; Masahiro Nakano; Atsutoshi Kumagai; Yasutoshi Ida; Akisato Kimura; Naonori Ueda (IEEE Big Data, 2022)

Transfer Anomaly Detection for Maximizing the Partial AUC. - Atsutoshi Kumagai; Tomoharu Iwata; Taishi Nishiyama; Yasuhiro Fujiwara (IJCNN, 2022)

Learning Optimal Priors for Task-Invariant Representations in Variational Autoencoders. - Hiroshi Takahashi; Tomoharu Iwata; Atsutoshi Kumagai; Sekitoshi Kanai; Masanori Yamada; Yuki Yamanaka; Hisashi Kashima (KDD, 2022)

Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks. - Daiki Chijiwa; Shin'ya Yamaguchi; Atsutoshi Kumagai; Yasutoshi Ida (NeurIPS, 2022)

Sharing Knowledge for Meta-learning with Feature Descriptions. - Tomoharu Iwata; Atsutoshi Kumagai (NeurIPS, 2022)

Few-shot Learning for Feature Selection with Hilbert-Schmidt Independence Criterion. - Atsutoshi Kumagai; Tomoharu Iwata; Yasutoshi Ida; Yasuhiro Fujiwara (NeurIPS, 2022)

Covariance-aware Feature Alignment with Pre-computed Source Statistics for Test-time Adaptation. - Kazuki Adachi; Shin'ya Yamaguchi; Atsutoshi Kumagai (CoRR, 2022)

Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space. - Tomoharu Iwata; Atsutoshi Kumagai (CoRR, 2022)

Fast Regularized Discrete Optimal Transport with Group-Sparse Regularizers. - Yasutoshi Ida; Sekitoshi Kanai; Kazuki Adachi; Atsutoshi Kumagai; Yasuhiro Fujiwara (AAAI, 2023)

Fast Block Coordinate Descent for Non-Convex Group Regularizations. - Yasutoshi Ida; Sekitoshi Kanai; Atsutoshi Kumagai (AISTATS, 2023)

Meta-learning for Robust Anomaly Detection. - Atsutoshi Kumagai; Tomoharu Iwata; Hiroshi Takahashi; Yasuhiro Fujiwara (AISTATS, 2023)

Efficient Network Representation Learning via Cluster Similarity. - Yasuhiro Fujiwara; Yasutoshi Ida; Atsutoshi Kumagai; Masahiro Nakano; Akisato Kimura; Naonori Ueda (DASFAA (3), 2023)

Covariance-Aware Feature Alignment with Pre-Computed Source Statistics for Test-Time Adaptation to Multiple Image Corruptions. - Kazuki Adachi; Shin'ya Yamaguchi; Atsutoshi Kumagai (ICIP, 2023)

Regularizing Neural Networks with Meta-Learning Generative Models. - Shin'ya Yamaguchi; Daiki Chijiwa; Sekitoshi Kanai; Atsutoshi Kumagai; Hisashi Kashima (NeurIPS, 2023)

Meta-learning of semi-supervised learning from tasks with heterogeneous attribute spaces. - Tomoharu Iwata; Atsutoshi Kumagai (CoRR, 2023)

Zero-Shot Task Adaptation with Relevant Feature Information. - Atsutoshi Kumagai; Tomoharu Iwata; Yasuhiro Fujiwara (AAAI, 2024)

Malicious Log Detection Using Machine Learning to Maximize the Partial AUC. - Taishi Nishiyama; Atsutoshi Kumagai; Akinori Fujino; Kazunori Kamiya (CCNC, 2024)

Partial AUC Maximization for Security Log Analysis Robust to Overfitting and Noisy Labels. - Taishi Nishiyama; Atsutoshi Kumagai; Akinori Fujino; Kazunori Kamiya (GLOBECOM, 2024)

Fast Iterative Hard Thresholding Methods with Pruning Gradient Computations. - Yasutoshi Ida; Sekitoshi Kanai; Atsutoshi Kumagai; Tomoharu Iwata; Yasuhiro Fujiwara (NeurIPS, 2024)

AUC Maximization under Positive Distribution Shift. - Atsutoshi Kumagai; Tomoharu Iwata; Hiroshi Takahashi; Taishi Nishiyama; Yasuhiro Fujiwara (NeurIPS, 2024)

Analysis of Linear Mode Connectivity via Permutation-Based Weight Matching. - Akira Ito 002; Masanori Yamada; Atsutoshi Kumagai (CoRR, 2024)

Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data. - Hiroshi Takahashi; Tomoharu Iwata; Atsutoshi Kumagai; Yuki Yamanaka (CoRR, 2024)

Meta-learning for Positive-unlabeled Classification. - Atsutoshi Kumagai; Tomoharu Iwata; Yasuhiro Fujiwara (CoRR, 2024)

Meta-learning to calibrate Gaussian processes with deep kernels for regression uncertainty estimation. - Tomoharu Iwata; Atsutoshi Kumagai (Neurocomputing, 2024)

Efficient Algorithm for K-Multiple-Means. - Yasuhiro Fujiwara; Atsutoshi Kumagai; Yasutoshi Ida; Masahiro Nakano; Makoto Nakatsuji; Akisato Kimura (Proc. ACM Manag. Data, 2024)

Meta-learning from Heterogeneous Tensors for Few-shot Tensor Completion. - Tomoharu Iwata; Atsutoshi Kumagai (AISTATS, 2025)

Meta-learning Task-specific Regularization Weights for Few-shot Linear Regression. - Tomoharu Iwata; Atsutoshi Kumagai; Yasutoshi Ida (AISTATS, 2025)

Importance-weighted Positive-unlabeled Learning for Distribution Shift Adaptation. - Atsutoshi Kumagai; Tomoharu Iwata; Hiroshi Takahashi; Taishi Nishiyama; Yasuhiro Fujiwara (AISTATS, 2025)

Analysis of Linear Mode Connectivity via Permutation-Based Weight Matching: With Insights into Other Permutation Search Methods. - Akira Ito 002; Masanori Yamada; Atsutoshi Kumagai (ICLR, 2025)

Test-time Adaptation for Regression by Subspace Alignment. - Kazuki Adachi; Shin'ya Yamaguchi; Atsutoshi Kumagai; Tomoki Hamagami (ICLR, 2025)

Positive-Unlabeled Diffusion Models for Preventing Sensitive Data Generation. - Hiroshi Takahashi; Tomoharu Iwata; Atsutoshi Kumagai; Yuuki Yamanaka; Tomoya Yamashita (ICLR, 2025)

Fast Proximal Gradient Methods with Node Pruning for Tree-Structured Sparse Regularization. - Yasutoshi Ida; Sekitoshi Kanai; Atsutoshi Kumagai; Tomoharu Iwata; Yasuhiro Fujiwara (ECML/PKDD (5), 2025)

Transfer learning with pre-trained conditional generative models. - Shin'ya Yamaguchi; Sekitoshi Kanai; Atsutoshi Kumagai; Daiki Chijiwa; Hisashi Kashima (Mach. Learn., 2025)

Meta-learning Representations for Learning from Multiple Annotators. - Atsutoshi Kumagai; Tomoharu Iwata; Taishi Nishiyama; Yasutoshi Ida; Yasuhiro Fujiwara (CoRR, 2025)

Do We Really Need Permutations? Impact of Width Expansion on Linear Mode Connectivity. - Akira Ito 002; Masanori Yamada; Daiki Chijiwa; Atsutoshi Kumagai (CoRR, 2025)