Handling incomplete matrix data via continuous-valued infinite relational model. - Tomohiko Suzuki; Takuma Nakamura; Yasutoshi Ida; Takashi Matsumoto 001 (ICASSP, 2012)

Label-Related/Unrelated Topic Switching Model: A Partially Labeled Topic Model Handling Infinite Label-Unrelated Topics. - Yasutoshi Ida; Takuma Nakamura; Takashi Matsumoto 001 (ACPR, 2013)

Domain-dependent/independent topic switching model for online reviews with numerical ratings. - Yasutoshi Ida; Takuma Nakamura; Takashi Matsumoto 001 (CIKM, 2013)

Adaptive Message Update for Fast Affinity Propagation. - Yasuhiro Fujiwara; Makoto Nakatsuji; Hiroaki Shiokawa; Yasutoshi Ida; Machiko Toyoda (KDD, 2015)

Fast Lasso Algorithm via Selective Coordinate Descent. - Yasuhiro Fujiwara; Yasutoshi Ida; Hiroaki Shiokawa; Sotetsu Iwamura (AAAI, 2016)

Controlling Exploration Improves Training for Deep Neural Networks. - Yasutoshi Ida; Yasuhiro Fujiwara; Sotetsu Iwamura (CoRR, 2016)

Fast Algorithm for the Lasso based L1-Graph Construction. - Yasuhiro Fujiwara; Yasutoshi Ida; Junya Arai; Mai Nishimura; Sotetsu Iwamura (Proc. VLDB Endow., 2016)

Adaptive Learning Rate via Covariance Matrix Based Preconditioning for Deep Neural Networks. - Yasutoshi Ida; Yasuhiro Fujiwara; Sotetsu Iwamura (IJCAI, 2017)

Adaptive Data Pruning for Support Vector Machines. - Yasuhiro Fujiwara; Junya Arai; Sekitoshi Kanai; Yasutoshi Ida; Naonori Ueda (IEEE BigData, 2018)

Efficient Data Point Pruning for One-Class SVM. - Yasuhiro Fujiwara; Sekitoshi Kanai; Junya Arai; Yasutoshi Ida; Naonori Ueda (AAAI, 2019)

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

Network Implosion: Effective Model Compression for ResNets via Static Layer Pruning and Retraining. - Yasutoshi Ida; Yasuhiro Fujiwara (IJCNN, 2019)

Fast Sparse Group Lasso. - Yasutoshi Ida; Yasuhiro Fujiwara; Hisashi Kashima (NeurIPS, 2019)

Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks. - Sekitoshi Kanai; Yasutoshi Ida; Yasuhiro Fujiwara; Masanori Yamada; Shuichi Adachi (AAAI, 2020)

Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance. - Yasutoshi Ida; Sekitoshi Kanai; Yasuhiro Fujiwara; Tomoharu Iwata; Koh Takeuchi 001; Hisashi Kashima (ICML, 2020)

Improving Generalization Performance of Adaptive Learning Rate by Switching from Block Diagonal Matrix Preconditioning to SGD. - Yasutoshi Ida; Yasuhiro Fujiwara (IJCNN, 2020)

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

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

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

Constraining Logits by Bounded Function for Adversarial Robustness. - Sekitoshi Kanai; Masanori Yamada; Shin'ya Yamaguchi; Hiroshi Takahashi; Yasutoshi Ida (IJCNN, 2021)

Pruning Randomly Initialized Neural Networks with Iterative Randomization. - Daiki Chijiwa; Shin'ya Yamaguchi; Yasutoshi Ida; Kenji Umakoshi; Tomohiro Inoue (NeurIPS, 2021)

Smoothness Analysis of Loss Functions of Adversarial Training. - Sekitoshi Kanai; Masanori Yamada; Hiroshi Takahashi; Yuki Yamanaka; Yasutoshi Ida (CoRR, 2021)

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

Algorithms for Accelerating Machine Learning with Wide and Deep Models. - Yasutoshi Ida (2021)

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

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

Switching One-Versus-the-Rest Loss to Increase the Margin of Logits for Adversarial Robustness. - Sekitoshi Kanai; Shin'ya Yamaguchi; Masanori Yamada; Hiroshi Takahashi; Yasutoshi Ida (CoRR, 2022)

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

Fast Saturating Gate for Learning Long Time Scales with Recurrent Neural Networks. - Kentaro Ohno; Sekitoshi Kanai; Yasutoshi Ida (AAAI, 2023)

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

One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training. - Sekitoshi Kanai; Shin'ya Yamaguchi; Masanori Yamada; Hiroshi Takahashi; Kentaro Ohno; Yasutoshi Ida (ICML, 2023)

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

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

Evaluating Time-Series Training Dataset through Lens of Spectrum in Deep State Space Models. - Sekitoshi Kanai; Yasutoshi Ida; Kazuki Adachi; Mihiro Uchida; Tsukasa Yoshida; Shin'ya Yamaguchi (CoRR, 2024)

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

Relationship Between Nonsmoothness in Adversarial Training, Constraints of Attacks, and Flatness in the Input Space. - Sekitoshi Kanai; Masanori Yamada; Hiroshi Takahashi; Yuki Yamanaka; Yasutoshi Ida (IEEE Trans. Neural Networks Learn. Syst., 2024)

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

Zero-shot Concept Bottleneck Models. - Shin'ya Yamaguchi; Kosuke Nishida; Daiki Chijiwa; Yasutoshi Ida (CoRR, 2025)

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