38 citations · 148 across the 20 of their papers we have counts for
4 papers · 1 filter
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…