Y. Ida, S. Kanai, A. Kumagai, T. Iwata, Y. Fujiwara,
Fast Proximal Gradient Methods with Node Pruning for Tree-Structured Sparse Regularization,
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2025, to appear.

S. Kanai, Y. Ida, K. Adachi, M. Uchida, T. Yoshida, S. Yamaguchi,
Evaluation of Time-Series Training Dataset through Lens of Spectrum of Deep State Space Models,
International Joint Conference on Neural Networks (IJCNN), 2025, to appear.

T. Iwata, A. Kumagai, Y. Ida,
Meta-learning Task-specific Regularization Weights for Few-shot Linear Regression,
International Conference on Artificial Intelligence and Statistics (AISTATS), 2025.

Y. Ida, S. Kanai, A. Kumagai, T. Iwata, Y. Fujiwara,
Fast Iterative Hard Thresholding Methods with Pruning Gradient Computations,
Neural Information Processing Systems (NeurIPS), 2024.
[paper] [slide]

Y. Fujiwara, A. Kumagai, Y. Ida, M. Nakano, M. Nakatsuji, A. Kimura,
Efficient Algorithm for K-Multiple-Means,
Special Interest Group on Management of Data (SIGMOD), 2024.
[paper]

S. Kanai, S. Yamaguchi, M. Yamada, H. Takahashi, K. Ohno, Y. Ida,
One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training,
International Conference on Machine Learning (ICML), 2023.
[paper]

Y. Ida, S. Kanai, A. Kumagai,
Fast Block Coordinate Descent for Non-Convex Group Regularizations,
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.
[paper]

Y. Fujiwara, Y. Ida, A. Kumagai, M. Nakano, A. Kimura, N. Ueda,
Efficient Network Representation Learning via Cluster Similarity,
International Conference on Database Systems for Advanced Applications (DASFAA), 2023.
[paper]

Y. Ida, S. Kanai, K. Adachi, A. Kumagai, Y. Fujiwara,
Fast Regularized Discrete Optimal Transport with Group-sparse Regularizers,
Association for the Advancement of Artificial Intelligence (AAAI), 2023.
[preprint] [slide] [poster]

K. Ohno, S. Kanai, Y. Ida,
Fast Saturating Gate for Learning Long Time Scales with Recurrent Neural Networks,
Association for the Advancement of Artificial Intelligence (AAAI), 2023.
[preprint]

Y. Fujiwara, M. Nakano, A.Kumagai, Y. Ida, A. Kimura, N. Ueda,
Fast Binary Network Hashing via Graph Clustering,
IEEE BigData, 2022.

A. Kumagai, T. Iwata, Y. Ida, Y. Fujiwara,
Meta-learning for Feature Selection with Hilbert-Schmidt Independence Criterion,
Neural Information Processing Systems (NeurIPS), 2022.

D. Chijiwa, S. Yamaguchi, A. Kumagai, Y. Ida,
Meta-ticket: Finding Optimal Subnetworks for Few-shot Learning within Randomly Initialized Neural Networks,
Neural Information Processing Systems (NeurIPS), 2022.

Y, Fujiwara, Y. Ida, A. Kumagai, S. Kanai, N. Ueda,
Fast and Accurate Anchor Graph-based Label Prediction,
International Conference on Information and Knowledge Management (CIKM), 2021.

D. Chijiwa, S. Yamaguchi, Y. Ida, K. Umakoshi, T. Inoue,
Pruning Randomly Initialized Neural Networks with Iterative Randomization,
Neural Information Processing Systems (NeurIPS, spotlight), 2021.
[paper]

S. Kanai, M. Yamada, S. Yamaguchi, H. Takahashi, Y. Ida,
Constraining Logits by Bounded Function for Adversarial Robustness,
International Joint Conference on Neural Networks (IJCNN), 2021.

Y. Fujiwara, Y. Ida, S, Kanai, A. Kumagai, N. Ueda,
Fast Similarity Computation for t-SNE,
IEEE International Conference on Data Engineering (ICDE), 2021.

Y. Ida, S. Kanai, Y. Fujiwara, T. Iwata, K. Takeuchi, H. Kashima,
Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance,
International Conference on Machine Learning (ICML), 2020.
[paper] [slide]

Y. Fujiwara, A. Kumagai, S. Kanai, Y. Ida, T. Kawanishi, N. Ueda,
Efficient Algorithm for the b-Matching Graph,
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020.
[paper]

Y. Ida, Y. Fujiwara,
Improving Generalization Performance of Adaptive Learning Rate by Switching from Block Diagonal Matrix Preconditioning to SGD,
International Joint Conference on Neural Networks (IJCNN), 2020.
[paper]

S. Kanai, Y. Ida, Y. Fujiwara, M. Yamada, S. Adachi,
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks,
Association for the Advancement of Artificial Intelligence (AAAI), 2020.
[paper(pdf)]

Y. Ida, Y. Fujiwara, H. Kashima,
Fast Sparse Group Lasso,
Neural Information Processing Systems (NeurIPS), 2019.
[paper] [slide]

Y. Fujiwara, Y. Ida, S. Kanai, A. Kumagai, J. Arai, N. Ueda,
Fast Random Forest Algorithm via Incremental Upper Bound,
International Conference on Information and Knowledge Management (CIKM), 2019.

Y. Fujiwara, S. Kanai, J. Arai, Y. Ida, N. Ueda,
Efficient Data Point Pruning for One-Class SVM,
Association for the Advancement of Artificial Intelligence (AAAI), 2019.

Y. Ida, Y. Fujiwara,
Network Implosion: Effective Model Compression for ResNets via Static Layer Pruning and Retraining,
International Joint Conference on Neural Networks (IJCNN), 2019.
[paper]

Y. Fujiwara, J. Arai, S. Kanai, Y. Ida, N. Ueda,
Adaptive Data Pruning for Support Vector Machines,
IEEE BigData, 2018.

Y. Ida, Y. Fujiwara, S. Iwamura,
Adaptive Learning Rate via Covariance Matrix Based Preconditioning for Deep Neural Networks,
International Joint Conference on Artificial Intelligence (IJCAI), 2017.
[paper] [slide]

Y. Fujiwara, Y. Ida, H. Shiokawa, S. Iwamura,
Fast Lasso Algorithm via Selective Coordinate Descent,
Association for the Advancement of Artificial Intelligence (AAAI), 2016.

Y. Fujiwara, M. Nakatsuji, Y. Ida, M. Toyoda,
Adaptive Message Update for Fast Affinity Propagation,
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016.

Y. Ida, T. Nakamura, T. Matsumoto,
Domain-Dependent/Independent Topic Switching Model for Online Reviews with Numerical Ratings,
ACM International Conference on Information and Knowledge Management (CIKM), 2013.
[paper]

H. Miyashita, T. Suzuki, T. Nakamura, Y. Ida, Takashi Matsumoto, T. Kaburagi,
Nonparametric Bayes-based Heterogeneous "Drosophila melanogaster" Gene Regulatory Network Inference: T-Process Regression,
Artificial Intelligence and Applications (AIA), 2013. Best Student Paper Award.

T. Suzuki, T. Nakamura, Y. Ida, T. Matsumoto,
Handling Incomplete Matrix Data Via Continuous-Valued Infinite Relational Model,
International Conference on Acoustic, Speech, and Signal Processing (ICASSP), 2012.

Y. Fujiwara, Y. Ida, A. Kumagai, M. Nakano, A. Kimura, N. Ueda,
Efficient Network Representation Learning via Cluster Similarity,
Data Science and Engineering (DSE), 2023.
[paper(pdf)]

S. Kanai, M. Yamada, H. Takahashi, Y. Yamanaka, Y. Ida,
Relationship between Non-smoothness in Adversarial Training, Constraints of Attacks, and Flatness in the Input Space,
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023.
[paper(pdf)]

Y. Fujiwara, S. Kanai, Y. Ida, A. Kumagai, N. Ueda,
Fast Algorithm for Anchor Graph Hashing,
Proceedings of the Very Large Data Bases (PVLDB), 2021.
[paper(pdf)]

Y. Fujiwara, Y. Ida, J. Arai, M. Nishimura, S. Iwamura,
Fast Algorithm for the Lasso based L1-Graph Construction,
Proceedings of the Very Large Data Bases (PVLDB), 2016.
[paper]

Y. Fukuda, Y. Ida, T. Matsumoto, N. Takemura, S. Kaoru,
A Bayesian Algorithm for Anxiety Index Prediction Based on CerebralBlood Oxygenation in the Prefrontal Cortex Measured by Near Infrared Spectroscopy,
IEEE Journal of Translational Engineering in Health and Medicine (JTEHM), 2014.
[paper]

Y. Ida, Y. Fujiwara, H. Kashima,
Fast Block Coordinate Descent for Sparse Group Lasso,
The Japanese Society for Artificial Intelligence, Vol. 36, No. 1, pp. A-JB1_1-11, 2021.
[paper]

Y. Ida, Y. Fujiwara,
Model Compression for ResNet via Layer Erasure and Re-training,
The Japanese Society for Artificial Intelligence, Vol. 35, No. 3, pp. C-JA3_1-10, 2020.
[paper]

Y. Ida, T. Nakamura, T. Matsumoto,
Label-Related/Unrelated Topic Switching Model: A Partially Labeled Topic Model Handling Infinite Label-unrelated Topics,
Recent Advances in Computer Vision and Pattern Recognition (RACVPR, Workshop on ACPR), 2013.