38 citations · 148 across the 20 of their papers we have counts for
8 papers · 1 filter
A More Accurate Algorithm Comparison through A/B Testing using Offline Evaluation Methods
Koki Konishi, Masataka Ushiku, Yuta Saito
A/B testing is the gold standard for selecting the better algorithm in online services. While offline evaluation has attracted attention as a safer alternative due to the high expe…
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…
Off-Policy Evaluation for Large Action Spaces via Embeddings
Yuta Saito, Thorsten Joachims
Off-policy evaluation (OPE) in contextual bandits has seen rapid adoption in real-world systems, since it enables offline evaluation of new policies using only historic log data. U…
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 (…
Optimal Off-Policy Evaluation from Multiple Logging Policies
Nathan Kallus, Yuta Saito, Masatoshi Uehara
We study off-policy evaluation (OPE) from multiple logging policies, each generating a dataset of fixed size, i.e., stratified sampling. Previous work noted that in this setting th…
Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation
Yuta Saito, Shunsuke Aihara, Megumi Matsutani +1
Off-policy evaluation (OPE) aims to estimate the performance of hypothetical policies using data generated by a different policy. Because of its huge potential impact in practice,…
Unbiased Lift-based Bidding System
Daisuke Moriwaki, Yuta Hayakawa, Isshu Munemasa +2
Conventional bidding strategies for online display ad auction heavily relies on observed performance indicators such as clicks or conversions. A bidding strategy naively pursuing t…
Efficient Hyperparameter Optimization under Multi-Source Covariate Shift
Masahiro Nomura, Yuta Saito
A typical assumption in supervised machine learning is that the train (source) and test (target) datasets follow completely the same distribution. This assumption is, however, ofte…