8 citations · 8 across the 10 of their papers we have counts for
7 papers · 1 filter
Combinatorial Allocation Bandits with Nonlinear Arm Utility
Yuki Shibukawa, Koichi Tanaka, Yuta Saito +1
A matching platform is a system that matches participants of different types, such as companies and job-seekers. In such a platform, maximizing matches may concentrate assignments…
Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching
Ren Kishimoto, Rikiya Takehi, Koichi Tanaka +4
On two-sided matching platforms such as online dating and recruiting, recommendation algorithms often aim to maximize the total number of matches. However, this objective creates a…
A General Framework for Off-Policy Learning with Partially-Observed Reward
Rikiya Takehi, Masahiro Asami, Kosuke Kawakami +1
Off-policy learning (OPL) in contextual bandits aims to learn a decision-making policy that maximizes the target rewards by using only historical interaction data collected under p…
Prompt Optimization with Logged Bandit Data
Haruka Kiyohara, Daniel Yiming Cao, Yuta Saito +1
We study how to use naturally available user feedback, such as clicks, to optimize large language model (LLM) pipelines for generating personalized sentences using prompts. Naive a…
Hyperparameter Optimization Can Even be Harmful in Off-Policy Learning and How to Deal with It
Yuta Saito, Masahiro Nomura
There has been a growing interest in off-policy evaluation in the literature such as recommender systems and personalized medicine. We have so far seen significant progress in deve…
Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation
Haruka Kiyohara, Ren Kishimoto, Kosuke Kawakami +3
Off-Policy Evaluation (OPE) aims to assess the effectiveness of counterfactual policies using only offline logged data and is often used to identify the top-k promising policies fo…
SCOPE-RL: A Python Library for Offline Reinforcement Learning and Off-Policy Evaluation
Haruka Kiyohara, Ren Kishimoto, Kosuke Kawakami +3
This paper introduces SCOPE-RL, a comprehensive open-source Python software designed for offline reinforcement learning (offline RL), off-policy evaluation (OPE), and selection (OP…