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Off-Policy Evaluation of Ranking Policies under Diverse User Behavior
Ranking interfaces are everywhere in online platforms. There is thus an ever growing interest in their Off-Policy Evaluation (OPE), …
Haruka Kiyohara, Tatsuya Matsuhiro, Yusuke Narita, Nobuyuki Shimizu, Yasuo Yamamoto, Yuta Saito
引用 コード ポスター スライド arXiv Proceedings
Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling
We study off-policy evaluation (OPE) of contextual bandit policies for large discrete action spaces where conventional …
Yuta Saito, Qingyang Ren, Thorsten Joachims
引用 コード ポスター スライド arXiv Proceedings
Policy-Adaptive Estimator Selection for Off-Policy Evaluation
Off-policy evaluation (OPE) aims to accurately evaluate the performance of counterfactual policies using only offline logged data. …
Takuma Udagawa, Haruka Kiyohara, Yusuke Narita, Yuta Saito, Kei Tateno
引用 コード スライド arXiv
Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking
Rankings have become the primary interface of many two-sided markets. Many have noted that the rankings not only affect the …
Yuta Saito, Thorsten Joachims
引用 コード 動画 スライド arXiv 会議録
Off-Policy Evaluation for Large Action Spaces via Embeddings
Off-policy evaluation (OPE) in contextual bandits has seen rapid adoption in real-world systems, since it enables offline evaluation of …
Yuta Saito, Thorsten Joachims
引用 コード 動画 スライド arXiv Proceedings
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