齋藤優太
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Towards Resolving Propensity Contradiction in Offline Recommender Learning
We study offline recommender learning from explicit rating feedback in the presence of selection bias. A current promising solution for …
Yuta Saito
,
Masahiro Nomura
引用
Proceedings
Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model
In real-world recommender systems and search engines, optimizing ranking decisions to present a ranked list of relevant items is …
Haruka Kiyohara
,
Yuta Saito
,
Tatsuya Matsuhiro
,
Yusuke Narita
,
Nobuyuki Shimizu
,
Yasuo Yamamoto
引用
コード
arXiv
会議録
Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation
Off-policy evaluation (OPE) aims to estimate the performance of hypothetical policies using data generated by a different policy. …
Yuta Saito
,
Shunsuke Aihara
,
Megumi Matsutani
,
Yusuke Narita
引用
コード
データセット
会議録
arXiv
A Real-World Implementation of Unbiased Lift-based Bidding System
Daisuke Moriwaki
,
Yuta Hayakawa
,
Isshu Munemasa
,
Yuta Saito
,
Akira Matsui
,
Masashi Shibata
引用
Efficient Hyperparameter Optimization under Multi-Source Covariate Shift
A typical assumption in supervised machine learning is that the train (source) and test (target) datasets follow completely the same …
Masahiro Nomura
,
Yuta Saito
引用
コード
会議録
arXiv
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
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