
Y. Ida, S. Kanai, K. Adachi, A. Kumagai, Y. Fujiwara,
Fast Regularized Discrete Optimal Transport with Groupsparse Regularizers,
Association for the Advancement of Artificial Intelligence (AAAI), 2023, to appear.

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, to appear.

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,
Metalearning for Feature Selection with HilbertSchmidt Independence Criterion,
Neural Information Processing Systems (NeurIPS), 2022.

D. Chijiwa, S. Yamaguchi, A. Kumagai, Y. Ida,
Metaticket: Finding Optimal Subnetworks for Fewshot 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 Graphbased 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 tSNE,
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 bMatching 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 OneClass 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,
DomainDependent/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 Bayesbased Heterogeneous "Drosophila melanogaster" Gene Regulatory Network Inference: TProcess Regression,
Artificial Intelligence and Applications (AIA), 2013. Best Student Paper Award.

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