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ORCIDSho Takemori
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2020 – today
- 2024
[c11]Sho Takemori, Yuhei Umeda, Aditya Gopalan:
Model-Based Best Arm Identification for Decreasing Bandits. AISTATS 2024: 1567-1575- [c10]Shrinivas Ramasubramanian, Harsh Rangwani, Sho Takemori, Kunal Samanta, Yuhei Umeda, Venkatesh Babu Radhakrishnan:
Selective Mixup Fine-Tuning for Optimizing Non-Decomposable Objectives. ICLR 2024 - [i6]Shrinivas Ramasubramanian, Harsh Rangwani, Sho Takemori, Kunal Samanta, Yuhei Umeda, Venkatesh Babu Radhakrishnan:
Selective Mixup Fine-Tuning for Optimizing Non-Decomposable Objectives. CoRR abs/2403.18301 (2024) - 2023
- [i5]Harsh Rangwani, Shrinivas Ramasubramanian, Sho Takemori, Kato Takashi, Yuhei Umeda, Venkatesh Babu Radhakrishnan:
Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics. CoRR abs/2304.14738 (2023) - 2022
- [c9]Sho Takemori:
Distributionally-Aware Kernelized Bandit Problems for Risk Aversion. ICML 2022: 20933-20959 - [c8]Harsh Rangwani, Shrinivas Ramasubramanian, Sho Takemori, Kato Takashi, Yuhei Umeda, Venkatesh Babu R.:
Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics. NeurIPS 2022 - 2021
- [c7]Masahiro Sato
, Janmajay Singh, Sho Takemori, Qian Zhang:
Causality-Aware Neighborhood Methods for Recommender Systems. ECIR (1) 2021: 603-618 - [c6]Sho Takemori, Masahiro Sato:
Approximation Theory Based Methods for RKHS Bandits. ICML 2021: 10076-10085 - [c5]Qian Zhang, Masahiro Sato, Sho Takemori, Tomoko Ohkuma:
Incorporating multi-level positive feedback to session-based nearest-neighbor. SAC 2021: 1371-1374 - 2020
- [c4]Masahiro Sato, Sho Takemori, Janmajay Singh, Tomoko Ohkuma:
Unbiased Learning for the Causal Effect of Recommendation. RecSys 2020: 378-387 - [c3]Masahiro Sato, Janmajay Singh, Sho Takemori, Takashi Sonoda, Qian Zhang, Tomoko Ohkuma:
Modeling user exposure with recommendation influence. SAC 2020: 1461-1464 - [c2]Sho Takemori, Masahiro Sato, Takashi Sonoda, Janmajay Singh, Tomoko Ohkuma:
Submodular Bandit Problem Under Multiple Constraints. UAI 2020: 191-200 - [i4]Sho Takemori, Masahiro Sato, Takashi Sonoda, Janmajay Singh, Tomoko Ohkuma:
Submodular Bandit Problem Under Multiple Constraints. CoRR abs/2006.00661 (2020) - [i3]Masahiro Sato, Sho Takemori, Janmajay Singh, Tomoko Ohkuma:
Unbiased Learning for the Causal Effect of Recommendation. CoRR abs/2008.04563 (2020) - [i2]Sho Takemori, Masahiro Sato:
Approximation Methods for Kernelized Bandits. CoRR abs/2010.12167 (2020) - [i1]Masahiro Sato, Sho Takemori, Janmajay Singh, Qian Zhang:
Causality-Aware Neighborhood Methods for Recommender Systems. CoRR abs/2012.09442 (2020)
2010 – 2019
- 2019
- [j4]Tomoyoshi Ibukiyama, Sho Takemori:
Construction of Theta Series of Any Vector-Valued Weight and Applications to Lifts and Congruences. Exp. Math. 28(1): 95-114 (2019) - [c1]Masahiro Sato, Janmajay Singh, Sho Takemori, Takashi Sonoda, Qian Zhang, Tomoko Ohkuma:
Uplift-based evaluation and optimization of recommenders. RecSys 2019: 296-304 - 2016
- [j3]Hidenori Katsurada, Sho Takemori:
Congruence Primes of the Kim-Ramakrishnan-Shahidi Lift. Exp. Math. 25(3): 332-346 (2016) - 2014
- [j2]Sho Takemori:
Congruence Relations for Siegel Modular Forms of Weight 47, 71, and 89. Exp. Math. 23(4): 423-428 (2014) - [j1]Sho Takemori:
On the computation of the determinant of vector-valued Siegel modular forms. LMS J. Comput. Math. 17(Theory): 247-256 (2014)
Coauthor Index
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