| 2026 | HRI | What You Reward Is What You Learn: Comparing Rewards for Online Speech Policy Optimization in Public HRI. | Sichao Song, Yuki Okafuji, Kaito Ariu, Amy Koike |
| 2025 | AAAI | Synchronization in Learning in Periodic Zero-Sum Games Triggers Divergence from Nash Equilibrium. | Yuma Fujimoto, Kaito Ariu, Kenshi Abe |
| 2025 | ACL | Theoretical Guarantees for Minimum Bayes Risk Decoding. | Yuki Ichihara, Yuu Jinnai, Kaito Ariu, Tetsuro Morimura, Eiji Uchibe |
| 2025 | ICLR | Boosting Perturbed Gradient Ascent for Last-Iterate Convergence in Games. | Kenshi Abe, Mitsuki Sakamoto, Kaito Ariu, Atsushi Iwasaki |
| 2025 | ICML | Revisiting Instance-Optimal Cluster Recovery in the Labeled Stochastic Block Model. | Kaito Ariu, Alexandre Proutire, Se-Young Yun |
| 2025 | NAACL | Regularized Best-of-N Sampling with Minimum Bayes Risk Objective for Language Model Alignment. | Yuu Jinnai, Tetsuro Morimura, Kaito Ariu, Kenshi Abe |
| 2025 | WSDM | Efficient Creative Selection in Online Advertising using Top-Two Thompson Sampling. | Daiki Katsuragawa, Yusuke Kaneko, Kaito Ariu, Kenshi Abe |
| 2024 | AAAI | Memory Asymmetry Creates Heteroclinic Orbits to Nash Equilibrium in Learning in Zero-Sum Games. | Yuma Fujimoto, Kaito Ariu, Kenshi Abe |
| 2024 | ACL | Hyperparameter-Free Approach for Faster Minimum Bayes Risk Decoding. | Yuu Jinnai, Kaito Ariu |
| 2024 | EMNLP | Filtered Direct Preference Optimization. | Tetsuro Morimura, Mitsuki Sakamoto, Yuu Jinnai, Kenshi Abe, Kaito Ariu |
| 2024 | ICML | Adaptively Perturbed Mirror Descent for Learning in Games. | Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto, Atsushi Iwasaki |
| 2024 | ICML | Model-Based Minimum Bayes Risk Decoding for Text Generation. | Yuu Jinnai, Tetsuro Morimura, Ukyo Honda, Kaito Ariu, Kenshi Abe |
| 2024 | ICML | Matroid Semi-Bandits in Sublinear Time. | Ruo-Chun Tzeng, Naoto Ohsaka, Kaito Ariu |
| 2024 | ICML | On Universally Optimal Algorithms for A/B Testing. | Po-An Wang, Kaito Ariu, Alexandre Proutire |
| 2023 | AISTATS | Last-Iterate Convergence with Full and Noisy Feedback in Two-Player Zero-Sum Games. | Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto, Kentaro Toyoshima, Atsushi Iwasaki |
| 2023 | IJCAI | Learning in Multi-Memory Games Triggers Complex Dynamics Diverging from Nash Equilibrium. | Yuma Fujimoto, Kaito Ariu, Kenshi Abe |
| 2023 | SIGIR | Exploration of Unranked Items in Safe Online Learning to Re-Rank. | Hiroaki Shiino, Kaito Ariu, Kenshi Abe, Riku Togashi |
| 2022 | ICML | Thresholded Lasso Bandit. | Kaito Ariu, Kenshi Abe, Alexandre Proutire |
| 2020 | AISTATS | Optimal Algorithms for Multiplayer Multi-Armed Bandits. | Po-An Wang, Alexandre Proutire, Kaito Ariu, Yassir Jedra, Alessio Russo |
| 2017 | AAAI | Chance-Constrained Path Planning with Continuous Time Safety Guarantees. | Kaito Ariu, Cheng Fang, Mrcio da Silva Arantes, Cludio Toledo, Brian Charles Williams |