38 citations · 88 across the 15 of their papers we have counts for
8 papers · 1 filter
Efficient Offline Learning of Ranking Policies via Top- Policy Decomposition
Ren Kishimoto, Koichi Tanaka, Haruka Kiyohara +4
Many recommender systems such as for e-commerce and news platforms aim to provide users with rankings they are likely to interact with. Off-Policy Learning (OPL) of ranking policie…
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…
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…
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…
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs
Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett +5
We study off-policy evaluation (OPE) for partially observable MDPs (POMDPs) with general function approximation. Existing methods such as sequential importance sampling estimators…
Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation
Haruka Kiyohara, Kosuke Kawakami, Yuta Saito
In recommender systems (RecSys) and real-time bidding (RTB) for online advertisements, we often try to optimize sequential decision making using bandit and reinforcement learning (…
Constrained Generalized Additive 2 Model with Consideration of High-Order Interactions
Akihisa Watanabe, Michiya Kuramata, Kaito Majima +3
In recent years, machine learning and AI have been introduced in many industrial fields. In fields such as finance, medicine, and autonomous driving, where the inference results of…