| Optimal regret analysis of thompson sampling in stochastic multi-armed bandit problem with multiple plays J Komiyama, J Honda, H Nakagawa International Conference on Machine Learning, 1152-1161, 2015 | 184 | 2015 |
| Polar coding without alphabet extension for asymmetric models J Honda, H Yamamoto IEEE Transactions on Information Theory 59 (12), 7829-7838, 2013 | 183 | 2013 |
| An Asymptotically Optimal Bandit Algorithm for Bounded Support Models. J Honda, A Takemura COLT, 67-79, 2010 | 161 | 2010 |
| Nonconvex optimization for regression with fairness constraints J Komiyama, A Takeda, J Honda, H Shimao International conference on machine learning, 2737-2746, 2018 | 125 | 2018 |
| Learning from positive and unlabeled data with a selection bias M Kato, T Teshima, J Honda International conference on learning representations, 2019 | 121 | 2019 |
| 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 | 98 | 2015 |
| 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 | 92 | 2018 |
| Optimality of Thompson sampling for Gaussian bandits depends on priors J Honda, A Takemura Artificial Intelligence and Statistics, 375-383, 2014 | 92 | 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 | 78 | 2011 |
| On the calibration of multiclass classification with rejection C Ni, N Charoenphakdee, J Honda, M Sugiyama Advances in Neural Information Processing Systems 32, 2586-2596, 2019 | 69 | 2019 |
| 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 | 63 | 2019 |
| 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 | 56 | 2015 |
| Copeland dueling bandit problem: Regret lower bound, optimal algorithm, and computationally efficient algorithm J Komiyama, J Honda, H Nakagawa International Conference on Machine Learning, 1235-1244, 2016 | 43 | 2016 |
| Bandit Algorithms Based on Thompson Sampling for Bounded Reward Distributions C Riou, J Honda Algorithmic Learning Theory, 777-826, 2020 | 41 | 2020 |
| Good arm identification via bandit feedback H Kano, J Honda, K Sakamaki, K Matsuura, A Nakamura, M Sugiyama Machine Learning 108 (5), 721-745, 2019 | 39 | 2019 |
| Almost instantaneous fixed-to-variable length codes H Yamamoto, M Tsuchihashi, J Honda IEEE Transactions on Information Theory 61 (12), 6432-6443, 2015 | 37 | 2015 |
| 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 | 36 | 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 | 33 | 2018 |
| Normal bandits of unknown means and variances W Cowan, J Honda, MN Katehakis The Journal of Machine Learning Research 18 (1), 5638-5665, 2017 | 33 | 2017 |
| 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 | 32 | 2015 |
