1 code implementation • 27 Mar 2024 • Shrinivas Ramasubramanian, Harsh Rangwani, Sho Takemori, Kunal Samanta, Yuhei Umeda, Venkatesh Babu Radhakrishnan
We find that current state-of-the-art empirical techniques offer sub-optimal performance on these practical, non-decomposable performance objectives.
1 code implementation • 28 Apr 2023 • Harsh Rangwani, Shrinivas Ramasubramanian, Sho Takemori, Kato Takashi, Yuhei Umeda, Venkatesh Babu Radhakrishnan
Using the proposed CSST framework, we obtain practical self-training methods (for both vision and NLP tasks) for optimizing different non-decomposable metrics using deep neural networks.
no code implementations • 17 Dec 2020 • Masahiro Sato, Sho Takemori, Janmajay Singh, Qian Zhang
In this work, we unify traditional neighborhood recommendation methods with the matching estimator, and develop robust ranking methods for the causal effect of recommendations.
no code implementations • 23 Oct 2020 • Sho Takemori, Masahiro Sato
The RKHS bandit problem (also called kernelized multi-armed bandit problem) is an online optimization problem of non-linear functions with noisy feedback.
no code implementations • 11 Aug 2020 • Masahiro Sato, Sho Takemori, Janmajay Singh, Tomoko Ohkuma
This paper proposes an unbiased learning framework for the causal effect of recommendation.
no code implementations • 1 Jun 2020 • Sho Takemori, Masahiro Sato, Takashi Sonoda, Janmajay Singh, Tomoko Ohkuma
Thus, motivated by diversified retrieval considering budget constraints, we introduce a submodular bandit problem under the intersection of $l$ knapsacks and a $k$-system constraint.