| Polar coding without alphabet extension for asymmetric models J Honda, H Yamamoto IEEE Transactions on Information Theory 59 (12), 7829-7838, 2013 | 134 | 2013 |
| An Asymptotically Optimal Bandit Algorithm for Bounded Support Models. J Honda, A Takemura COLT, 67-79, 2010 | 121 | 2010 |
| Optimal regret analysis of thompson sampling in stochastic multi-armed bandit problem with multiple plays J Komiyama, J Honda, H Nakagawa arXiv preprint arXiv:1506.00779, 2015 | 107 | 2015 |
| Optimality of Thompson sampling for Gaussian bandits depends on priors J Honda, A Takemura Artificial Intelligence and Statistics, 375-383, 2014 | 55 | 2014 |
| An asymptotically optimal policy for finite support models in the multiarmed bandit problem J Honda, A Takemura Machine Learning 85 (3), 361-391, 2011 | 55 | 2011 |
| Regret lower bound and optimal algorithm in dueling bandit problem J Komiyama, J Honda, H Kashima, H Nakagawa Conference on Learning Theory, 1141-1154, 2015 | 45 | 2015 |
| Nonconvex optimization for regression with fairness constraints J Komiyama, A Takeda, J Honda, H Shimao International conference on machine learning, 2737-2746, 2018 | 42 | 2018 |
| A fully adaptive algorithm for pure exploration in linear bandits L Xu, J Honda, M Sugiyama International Conference on Artificial Intelligence and Statistics, 843-851, 2018 | 30 | 2018 |
| Construction of polar codes for channels with memory R Wang, J Honda, H Yamamoto, R Liu, Y Hou 2015 IEEE Information Theory Workshop-Fall (ITW), 187-191, 2015 | 26 | 2015 |
| Exploring a potential energy surface by machine learning for characterizing atomic transport K Kanamori, K Toyoura, J Honda, K Hattori, A Seko, M Karasuyama, ... Physical Review B 97 (12), 125124, 2018 | 25 | 2018 |
| Non-asymptotic analysis of a new bandit algorithm for semi-bounded rewards J Honda, A Takemura The Journal of Machine Learning Research 16 (1), 3721-3756, 2015 | 25 | 2015 |
| Unsupervised domain adaptation based on source-guided discrepancy S Kuroki, N Charoenphakdee, H Bao, J Honda, I Sato, M Sugiyama Proceedings of the AAAI Conference on Artificial Intelligence 33, 4122-4129, 2019 | 24 | 2019 |
| Learning from positive and unlabeled data with a selection bias M Kato, T Teshima, J Honda International Conference on Learning Representations, 2018 | 23 | 2018 |
| Exact asymptotics for the random coding error probability J Honda 2015 IEEE International Symposium on Information Theory (ISIT), 91-95, 2015 | 21 | 2015 |
| Copeland dueling bandit problem: Regret lower bound, optimal algorithm, and computationally efficient algorithm J Komiyama, J Honda, H Nakagawa arXiv preprint arXiv:1605.01677, 2016 | 18 | 2016 |
| Almost instantaneous fixed-to-variable length codes H Yamamoto, M Tsuchihashi, J Honda IEEE Transactions on Information Theory 61 (12), 6432-6443, 2015 | 18 | 2015 |
| Regret lower bound and optimal algorithm in finite stochastic partial monitoring J Komiyama, J Honda, H Nakagawa Advances in Neural Information Processing Systems 28, 1792-1800, 2015 | 17 | 2015 |
| Normal bandits of unknown means and variances W Cowan, J Honda, MN Katehakis The Journal of Machine Learning Research 18 (1), 5638-5665, 2017 | 16 | 2017 |
| RSA meets DPA: recovering RSA secret keys from noisy analog data N Kunihiro, J Honda International Workshop on Cryptographic Hardware and Embedded Systems, 261-278, 2014 | 15 | 2014 |
| Worst-case redundancy of optimal binary AIFV codes and their extended codes W Hu, H Yamamoto, J Honda IEEE Transactions on Information Theory 63 (8), 5074-5086, 2017 | 14 | 2017 |
