| Selective sampling-based scalable sparse subspace clustering S Matsushima, M Brbic Advances in Neural Information Processing Systems 32, 2019 | 68 | 2019 |
| Wordrank: Learning word embeddings via robust ranking S Ji, H Yun, P Yanardag, S Matsushima, SVN Vishwanathan Conference on Empirical Methods in Natural Language Processing, 2016 | 64 | 2016 |
| ITC-UT: Tweet Categorization by Query Categorization for On-line Reputation Management. M Yoshida, S Matsushima, S Ono, I Sato, H Nakagawa CLEF (Notebook Papers/LABs/Workshops) 170, 2010 | 37 | 2010 |
| Exact passive-aggressive algorithm for multiclass classification using support class S Matsushima, N Shimizu, K Yoshida, T Ninomiya, H Nakagawa Proceedings of the 2010 SIAM international conference on data mining, 303-314, 2010 | 30 | 2010 |
| mdx: A cloud platform for supporting data science and cross-disciplinary research collaborations T Suzumura, A Sugiki, H Takizawa, A Imakura, H Nakamura, K Taura, ... 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf …, 2022 | 28 | 2022 |
| Linear support vector machines via dual cached loops S Matsushima, SVN Vishwanathan, AJ Smola Proceedings of the 18th ACM SIGKDD international conference on Knowledge …, 2012 | 25 | 2012 |
| Traffic risk mining from heterogeneous road statistics K Moriya, S Matsushima, K Yamanishi IEEE Transactions on Intelligent Transportation Systems 19 (11), 3662-3675, 2018 | 20 | 2018 |
| DS-MLR: exploiting double separability for scaling up distributed multinomial logistic regression P Raman, S Srinivasan, S Matsushima, X Zhang, H Yun, ... arXiv preprint arXiv:1604.04706, 2016 | 13 | 2016 |
| Scaling multinomial logistic regression via hybrid parallelism P Raman, S Srinivasan, S Matsushima, X Zhang, H Yun, ... Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 10 | 2019 |
| Distributed stochastic optimization of regularized risk via saddle-point problem S Matsushima, H Yun, X Zhang, SVN Vishwanathan Machine Learning and Knowledge Discovery in Databases: European Conference …, 2017 | 8 | 2017 |
| Feature-aware regularization for sparse online learning H Oiwa, S Matsushima, H Nakagawa Science China Information Sciences 57, 1-21, 2014 | 6 | 2014 |
| Frequency-aware truncated methods for sparse online learning H Oiwa, S Matsushima, H Nakagawa Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011 | 6 | 2011 |
| Traffic risk mining using partially ordered non-negative matrix factorization T Lee, S Matsushima, K Yamanishi 2016 IEEE international conference on data science and advanced analytics …, 2016 | 5 | 2016 |
| Grafting for combinatorial binary model using frequent itemset mining T Lee, S Matsushima, K Yamanishi Data Mining and Knowledge Discovery 34 (1), 101-123, 2020 | 4 | 2020 |
| Model selection for non-negative tensor factorization with minimum description length Y Fu, S Matsushima, K Yamanishi Entropy 21 (7), 632, 2019 | 4 | 2019 |
| Sparse graphical modeling via stochastic complexity K Miyaguchi, S Matsushima, K Yamanishi Proceedings of the 2017 SIAM International Conference on Data Mining, 723-731, 2017 | 4 | 2017 |
| Web behavior analysis using sparse non-negative matrix factorization A Demachi, S Matsushima, K Yamanishi 2016 IEEE International Conference on Data Science and Advanced Analytics …, 2016 | 4 | 2016 |
| Healing truncation bias: self-weighted truncation framework for dual averaging H Oiwa, S Matsushima, H Nakagawa 2012 IEEE 12th International Conference on Data Mining, 575-584, 2012 | 4 | 2012 |
| Totally corrective boosting with cardinality penalization VS Denchev, N Ding, S Matsushima, SVN Vishwanathan, H Neven arXiv preprint arXiv:1504.01446, 2015 | 3 | 2015 |
| Online Learning Under Capricious Feature Data Streams H Zhou, S Matsushima 人工知能学会全国大会論文集 第 37 回 (2023), 2D4GS201-2D4GS201, 2023 | 2 | 2023 |
S V N VishwanathanAssociate Professor of Statistics and Computer Science, Purdue UniversityVerified email at stat.purdue.edu
