Congruence Relations for Siegel Modular Forms of Weight 47, 71, and 89. - Sho Takemori (Exp. Math., 2014)

On the computation of the determinant of vector-valued Siegel modular forms. - Sho Takemori (LMS J. Comput. Math., 2014)

Congruence Primes of the Kim-Ramakrishnan-Shahidi Lift. - Hidenori Katsurada; Sho Takemori (Exp. Math., 2016)

Uplift-based evaluation and optimization of recommenders. - Masahiro Sato; Janmajay Singh; Sho Takemori; Takashi Sonoda; Qian Zhang 061; Tomoko Ohkuma (RecSys, 2019)

Construction of Theta Series of Any Vector-Valued Weight and Applications to Lifts and Congruences. - Tomoyoshi Ibukiyama; Sho Takemori (Exp. Math., 2019)

Unbiased Learning for the Causal Effect of Recommendation. - Masahiro Sato; Sho Takemori; Janmajay Singh; Tomoko Ohkuma (RecSys, 2020)

Modeling user exposure with recommendation influence. - Masahiro Sato; Janmajay Singh; Sho Takemori; Takashi Sonoda; Qian Zhang 061; Tomoko Ohkuma (SAC, 2020)

Submodular Bandit Problem Under Multiple Constraints. - Sho Takemori; Masahiro Sato; Takashi Sonoda; Janmajay Singh; Tomoko Ohkuma (UAI, 2020)

Approximation Methods for Kernelized Bandits. - Sho Takemori; Masahiro Sato (CoRR, 2020)

Causality-Aware Neighborhood Methods for Recommender Systems. - Masahiro Sato; Janmajay Singh; Sho Takemori; Qian Zhang 061 (ECIR (1), 2021)

Approximation Theory Based Methods for RKHS Bandits. - Sho Takemori; Masahiro Sato (ICML, 2021)

Incorporating multi-level positive feedback to session-based nearest-neighbor. - Qian Zhang 061; Masahiro Sato; Sho Takemori; Tomoko Ohkuma (SAC, 2021)

Distributionally-Aware Kernelized Bandit Problems for Risk Aversion. - Sho Takemori (ICML, 2022)

Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics. - Harsh Rangwani; Shrinivas Ramasubramanian; Sho Takemori; Kato Takashi; Yuhei Umeda; Venkatesh Babu R. (NeurIPS, 2022)

Model-Based Best Arm Identification for Decreasing Bandits. - Sho Takemori; Yuhei Umeda; Aditya Gopalan (AISTATS, 2024)

Selective Mixup Fine-Tuning for Optimizing Non-Decomposable Objectives. - Shrinivas Ramasubramanian; Harsh Rangwani; Sho Takemori; Kunal Samanta; Yuhei Umeda; Venkatesh Babu Radhakrishnan (ICLR, 2024)

Quantum Kernelized Bandits. - Yasunari Hikima; Kazunori Murao; Sho Takemori; Yuhei Umeda (UAI, 2024)

Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian Optimization. - Nobuo Namura; Sho Takemori (AAAI, 2025)