| Speaker invariant feature extraction for zero-resource languages with adversarial learning T Tsuchiya, N Tawara, T Ogawa, T Kobayashi 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 37 | 2018 |
| Nearly optimal best-of-both-worlds algorithms for online learning with feedback graphs S Ito, T Tsuchiya, J Honda Advances in Neural Information Processing Systems (NeurIPS 2022), 2022 | 28 | 2022 |
| Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification J Komiyama, T Tsuchiya, J Honda Advances in Neural Information Processing Systems (NeurIPS 2022), 2022 | 23 | 2022 |
| Best-of-Both-Worlds Algorithms for Partial Monitoring T Tsuchiya, S Ito, J Honda International Conference on Algorithmic Learning Theory (ALT 2023), 1484-1515, 2022 | 21 | 2022 |
| Adversarially robust multi-armed bandit algorithm with variance-dependent regret bounds S Ito, T Tsuchiya, J Honda Conference on Learning Theory, 1421-1422, 2022 | 19 | 2022 |
| Semi-supervised ordinal regression based on empirical risk minimization T Tsuchiya, N Charoenphakdee, I Sato, M Sugiyama Neural Computation 33 (12), 3361-3412, 2021 | 12 | 2021 |
| Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring T Tsuchiya, J Honda, M Sugiyama Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 8861-8871, 2020 | 10 | 2020 |
| Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds T Tsuchiya, S Ito, J Honda Advances in Neural Information Processing Systems (NeurIPS 2023), 2023 | 9 | 2023 |
| Further adaptive best-of-both-worlds algorithm for combinatorial semi-bandits T Tsuchiya, S Ito, J Honda International Conference on Artificial Intelligence and Statistics (AISTATS …, 2023 | 9 | 2023 |
| Follow-the-Perturbed-Leader Achieves Best-of-Both-Worlds for Bandit Problems J Honda, S Ito, T Tsuchiya International Conference on Algorithmic Learning Theory (ALT 2023), 726-754, 2023 | 7 | 2023 |
| Best-of-Both-Worlds Algorithms for Linear Contextual Bandits Y Kuroki, A Rumi, T Tsuchiya, F Vitale, N Cesa-Bianchi International Conference on Artificial Intelligence and Statistics, 1216-1224, 2024 | 4 | 2024 |
| Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds S Ito, T Tsuchiya, J Honda The Thirty Seventh Annual Conference on Learning Theory, 2522-2563, 2024 | 3* | 2024 |
| Online Structured Prediction with Fenchel–Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss S Sakaue, H Bao, T Tsuchiya, T Oki The Thirty Seventh Annual Conference on Learning Theory, 4458-4486, 2024 | 2 | 2024 |
| Revisiting Online Learning Approach to Inverse Linear Optimization: A FenchelYoung Loss Perspective and Gap-Dependent Regret Analysis S Sakaue, H Bao, T Tsuchiya arXiv preprint arXiv:2501.13648, 2025 | 1 | 2025 |
| Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring T Tsuchiya, S Ito, J Honda International Conference on Machine Learning, 48768-48790, 2024 | 1 | 2024 |
| Online Inverse Linear Optimization: Improved Regret Bound, Robustness to Suboptimality, and Toward Tight Regret Analysis S Sakaue, T Tsuchiya, H Bao, T Oki arXiv preprint arXiv:2501.14349, 2025 | | 2025 |
| Fast Rates in Stochastic Online Convex Optimization by Exploiting the Curvature of Feasible Sets T Tsuchiya, S Ito The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024 | | 2024 |
| Corrupted Learning Dynamics in Games T Tsuchiya, S Ito, H Luo arXiv preprint arXiv:2412.07120, 2024 | | 2024 |
| A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of and its Application to Best-of-Both-Worlds T Tsuchiya, S Ito arXiv preprint arXiv:2405.20028, 2024 | | 2024 |
| Fast Rates in Online Convex Optimization by Exploiting the Curvature of Feasible Sets T Tsuchiya, S Ito arXiv preprint arXiv:2402.12868, 2024 | | 2024 |
