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Evaluating the Robustness of Off-Policy Evaluation
Off-policy Evaluation (OPE), or offline evaluation in general, evaluates the performance of hypothetical policies leveraging only …
Yuta Saito, Takuma Udagawa, Haruka Kiyohara, Kazuki Mogi, Yusuke Narita, Kei Tateno
引用 コード スライド 会議録 arXiv
Optimal Off-Policy Evaluation from Multiple Logging Policies
We study off-policy evaluation (OPE) from multiple logging policies, each generating a dataset of fixed size, i.e., stratified …
Nathan Kallus, Yuta Saito, Masatoshi Uehara
引用 コード 会議録 arXiv
Doubly Robust Estimator for Ranking Metrics with Post-Click Conversions
Post-click conversion, a pre-defined action on a web service after a click, is an essential form of feedback, as it directly …
Yuta Saito
引用 コード スライド 会議録
Unbiased Pairwise Learning from Biased Implicit Feedback
Implicit feedback is prevalent in real-world scenarios and is widely used in the construction of recommender systems. However, the …
Yuta Saito
引用 コード 会議録
Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback
In most real-world recommender systems, the observed rating data are subject to selection bias, and the data are thus …
Yuta Saito
引用 コード スライド 会議録
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models
We study the model selection problem in mph{conditional average treatment effect} (CATE) prediction. Unlike previous works on this …
Yuta Saito, Shota Yasui
引用 コード スライド 会議録
Dual Learning Algorithm for Delayed Conversions
In display advertising, predicting the conversion rate (CVR), meaning the probability that a user takes a predefined action on an …
Yuta Saito, Gota Morishita, Shota Yasui
引用 スライド 会議録
Cost-Effective and Stable Policy Optimization Algorithm for Uplift Modeling with Multiple Treatments
Uplift modeling aims to optimize treatment policies and is a promising method for causal-based personalization in various domains such …
Yuta Saito, Hayato Sakata, Kazuhide Nakata
引用 会議録
Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback
Recommender systems widely use implicit feedback such as click data because of its general availability. Although the presence of …
Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, Kazuhide Nakata
引用 コード スライド 会議録
Doubly Robust Prediction and Evaluation Methods Improve Uplift Modeling for Observational Data
Uplift modeling aims to optimize treatment allocation by predicting the net effect of a treatment on each individual (ITE) and is …
Yuta Saito, Hayato Sakata, Kazuhide Nakata
引用 会議録