| 2025 | AAAI | Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian Optimization. | Nobuo Namura, Sho Takemori |
| 2025 | EMNLP | Adaptive LLM Routing under Budget Constraints. | Pranoy Panda, Raghav Magazine, Chaitanya Devaguptapu, Sho Takemori, Vishal Sharma |
| 2025 | ICML | Instance-Optimal Pure Exploration for Linear Bandits on Continuous Arms. | Sho Takemori, Yuhei Umeda, Aditya Gopalan |
| 2024 | AISTATS | Model-Based Best Arm Identification for Decreasing Bandits. | Sho Takemori, Yuhei Umeda, Aditya Gopalan |
| 2024 | ICLR | Selective Mixup Fine-Tuning for Optimizing Non-Decomposable Objectives. | Shrinivas Ramasubramanian, Harsh Rangwani, Sho Takemori, Kunal Samanta, Yuhei Umeda, Venkatesh Babu Radhakrishnan |
| 2024 | UAI | Quantum Kernelized Bandits. | Yasunari Hikima, Kazunori Murao, Sho Takemori, Yuhei Umeda |
| 2022 | ICML | Distributionally-Aware Kernelized Bandit Problems for Risk Aversion. | Sho Takemori |
| 2021 | ECIR | Causality-Aware Neighborhood Methods for Recommender Systems. | Masahiro Sato, Janmajay Singh, Sho Takemori, Qian Zhang |
| 2021 | ICML | Approximation Theory Based Methods for RKHS Bandits. | Sho Takemori, Masahiro Sato |
| 2021 | SAC | Incorporating multi-level positive feedback to session-based nearest-neighbor. | Qian Zhang, Masahiro Sato, Sho Takemori, Tomoko Ohkuma |
| 2020 | RecSys | Unbiased Learning for the Causal Effect of Recommendation. | Masahiro Sato, Sho Takemori, Janmajay Singh, Tomoko Ohkuma |
| 2020 | SAC | Modeling user exposure with recommendation influence. | Masahiro Sato, Janmajay Singh, Sho Takemori, Takashi Sonoda, Qian Zhang, Tomoko Ohkuma |
| 2020 | UAI | Submodular Bandit Problem Under Multiple Constraints. | Sho Takemori, Masahiro Sato, Takashi Sonoda, Janmajay Singh, Tomoko Ohkuma |
| 2019 | RecSys | Uplift-based evaluation and optimization of recommenders. | Masahiro Sato, Janmajay Singh, Sho Takemori, Takashi Sonoda, Qian Zhang, Tomoko Ohkuma |