57 citations · 106 across the 10 of their papers we have counts for
10 papers
Offline Contextual Bandits in the Presence of New Actions
Ren Kishimoto, Tatsuhiro Shimizu, Kazuki Kawamura +6
Automated decision-making algorithms drive applications such as recommendation systems and search engines. These algorithms often rely on off-policy contextual bandits or off-polic…
Off-Policy Evaluation for Ranking Policies under Deterministic Logging Policies
Koichi Tanaka, Kazuki Kawamura, Takanori Muroi +6
Off-Policy Evaluation (OPE) is an important practical problem in algorithmic ranking systems, where the goal is to estimate the expected performance of a new ranking policy using o…
Safely Exploring Novel Actions in Recommender Systems via Deployment-Efficient Policy Learning
Haruka Kiyohara, Yusuke Narita, Yuta Saito +2
In many real recommender systems, novel items are added frequently over time. The importance of sufficiently presenting novel actions has widely been acknowledged for improving lon…
Counterfactual Reciprocal Recommender Systems for User-to-User Matching
Kazuki Kawamura, Takuma Udagawa, Kei Tateno
Reciprocal recommender systems (RRS) in dating, gaming, and talent platforms require mutual acceptance for a match. Logged data, however, over-represents popular profiles due to pa…
Not Just What, But When: Integrating Irregular Intervals to LLM for Sequential Recommendation
Wei-Wei Du, Takuma Udagawa, Kei Tateno
Time intervals between purchasing items are a crucial factor in sequential recommendation tasks, whereas existing approaches focus on item sequences and often overlook by assuming…
Policy-Adaptive Estimator Selection for Off-Policy Evaluation
Takuma Udagawa, Haruka Kiyohara, Yusuke Narita +2
Off-policy evaluation (OPE) aims to accurately evaluate the performance of counterfactual policies using only offline logged data. Although many estimators have been developed, the…
Data-Driven Off-Policy Estimator Selection: An Application in User Marketing on An Online Content Delivery Service
Yuta Saito, Takuma Udagawa, Kei Tateno
Off-policy evaluation (OPE) is the method that attempts to estimate the performance of decision making policies using historical data generated by different policies without conduc…
Evaluating the Robustness of Off-Policy Evaluation
Yuta Saito, Takuma Udagawa, Haruka Kiyohara +3
Off-policy Evaluation (OPE), or offline evaluation in general, evaluates the performance of hypothetical policies leveraging only offline log data. It is particularly useful in app…
Finite-size Gap, Magnetization, and Entanglement of Deformed Fredkin Spin Chain
Takuma Udagawa, Hosho Katsura
We investigate ground- and excited-state properties of the deformed Fredkin spin chain proposed by Salberger, Zhang, Klich, Korepin, and the authors. This model is a one-parameter…
Deformed Fredkin Spin Chain with Extensive Entanglement
Olof Salberger, Takuma Udagawa, Zhao Zhang +3
We introduce a new spin chain which is a deformation of the Fredkin spin chain and has a phase transition between bounded and extensive entanglement entropy scaling. In this chain,…