Sho Takemori

According to our database, Sho Takemori authored at least 21 papers between 2014 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Best-Arm Identification-Based Trust Region Selection for Bayesian Optimization on Multimodal Functions.

Nobuo Namura, Sho Takemori

CoRR, May, 2026

2025

Instance-Optimal Pure Exploration for Linear Bandits on Continuous Arms.

Sho Takemori, Yuhei Umeda, Aditya Gopalan

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Adaptive LLM Routing under Budget Constraints.

Pranoy Panda, Raghav Magazine, Chaitanya Devaguptapu, Sho Takemori, Vishal Sharma

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2025, 2025

Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian Optimization.

Nobuo Namura, Sho Takemori

Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

2024

Quantum Kernelized Bandits.

Yasunari Hikima, Kazunori Murao, Sho Takemori, Yuhei Umeda

Proceedings of the Uncertainty in Artificial Intelligence, 2024

Selective Mixup Fine-Tuning for Optimizing Non-Decomposable Objectives.

Shrinivas Ramasubramanian, Harsh Rangwani, Sho Takemori, Kunal Samanta, Yuhei Umeda, Venkatesh Babu Radhakrishnan

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Model-Based Best Arm Identification for Decreasing Bandits.

Sho Takemori, Yuhei Umeda, Aditya Gopalan

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2022

Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics.

Harsh Rangwani, Shrinivas Ramasubramanian, Sho Takemori, Kato Takashi, Yuhei Umeda, Venkatesh Babu R.

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Distributionally-Aware Kernelized Bandit Problems for Risk Aversion.

Sho Takemori

Proceedings of the International Conference on Machine Learning, 2022

2021

Incorporating multi-level positive feedback to session-based nearest-neighbor.

Qian Zhang, Masahiro Sato, Sho Takemori, Tomoko Ohkuma

Proceedings of the SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing, 2021

Approximation Theory Based Methods for RKHS Bandits.

Sho Takemori, Masahiro Sato

Proceedings of the 38th International Conference on Machine Learning, 2021

Causality-Aware Neighborhood Methods for Recommender Systems.

Masahiro Sato, Janmajay Singh, Sho Takemori, Qian Zhang

Proceedings of the Advances in Information Retrieval, 2021

2020

Approximation Methods for Kernelized Bandits.

Sho Takemori, Masahiro Sato

CoRR, 2020

Submodular Bandit Problem Under Multiple Constraints.

Sho Takemori, Masahiro Sato, Takashi Sonoda, Janmajay Singh, Tomoko Ohkuma

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Modeling user exposure with recommendation influence.

Masahiro Sato, Janmajay Singh, Sho Takemori, Takashi Sonoda, Qian Zhang, Tomoko Ohkuma

Proceedings of the SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing, online event, [Brno, Czech Republic], March 30, 2020

Unbiased Learning for the Causal Effect of Recommendation.

Masahiro Sato, Sho Takemori, Janmajay Singh, Tomoko Ohkuma

Proceedings of the RecSys 2020: Fourteenth ACM Conference on Recommender Systems, 2020

2019

Construction of Theta Series of Any Vector-Valued Weight and Applications to Lifts and Congruences.

Tomoyoshi Ibukiyama, Sho Takemori

Exp. Math., 2019

Uplift-based evaluation and optimization of recommenders.

Masahiro Sato, Janmajay Singh, Sho Takemori, Takashi Sonoda, Qian Zhang, Tomoko Ohkuma

Proceedings of the 13th ACM Conference on Recommender Systems, 2019

2016

Congruence Primes of the Kim-Ramakrishnan-Shahidi Lift.

Hidenori Katsurada, Sho Takemori

Exp. Math., 2016

2014

On the computation of the determinant of vector-valued Siegel modular forms.

Sho Takemori

LMS J. Comput. Math., 2014

Congruence Relations for Siegel Modular Forms of Weight 47, 71, and 89.

Sho Takemori

Exp. Math., 2014