Publications

(2022). オフ方策評価の基礎と動向. 人工知能, Vol.37, No.4.

学会誌

(2022). Counterfactual Evaluation and Learning for Interactive Systems. In Proceedings of the 28th SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD).

引用 コード 会議録 Website

(2022). Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking. In Proceedings of the 28th SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (Acceptance rate=14.9%).

引用 コード 動画 スライド arXiv 会議録

(2022). Off-Policy Evaluation for Large Action Spaces via Embeddings. In Proceedings of 39th International Conference on Machine Learning (ICML) (Acceptance rate=21.9%).

引用 コード 動画 スライド arXiv Proceedings

(2022). Towards Resolving Propensity Contradiction in Offline Recommender Learning. In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI) (Acceptance rate=15%, Long Talk (top 25% of accepted papers)).

引用 arXiv

(2022). Unbiased Recommender Learning from Biased Graded Implicit Feedback. WSDM 2022 Workshop on Decision Making for Modern Information Retrieval System.

引用 PDF

(2022). Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model. In Proceedings of the 15th International Conference on Web Search and Data Mining (WSDM) (Acceptance rate=20.2%, Best Paper Runner-Up).

引用 コード arXiv 会議録

(2022). 会議報告:The 15th ACM Conference on Recommender Systems(RecSys 2021). 人工知能, Vol.37, No.1.

学会誌

(2021). Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation. In Proceedings of the Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks.

引用 コード データセット 会議録 arXiv

(2021). A Real-World Implementation of Unbiased Lift-based Bidding System. In Proceedings of the 2021 IEEE International Conference on Big Data (IEEE BigData).

引用

(2021). Efficient Hyperparameter Optimization under Multi-Source Covariate Shift. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM).

引用 コード 会議録 arXiv

(2021). Evaluating the Robustness of Off-Policy Evaluation. In Proceedings of the 15th ACM Conference on Recommender Systems (RecSys).

引用 コード スライド 会議録 arXiv

(2021). Counterfactual Learning and Evaluation for Recommender Systems. In Proceedings of the 15th ACM Conference on Recommender Systems (RecSys).

引用 コード プロジェクト 会議録

(2021). Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation. RecSys 2021 Workshop on Simulation Methods for Recommender Systems.

引用 arXiv

(2021). Optimal Off-Policy Evaluation from Multiple Logging Policies. In Proceedings of 38th International Conference on Machine Learning (ICML).

引用 コード 会議録 arXiv

(2020). 自然実験としてのアルゴリズム:機械学習・市場設計・公共政策への統一アプローチ. RIETI Discussion Paper Series 20-J-045.

ディスカッション・ペーパー

(2020). Doubly Robust Estimator for Ranking Metrics with Post-Click Conversions. In Proceedings of the 14th ACM Conference on Recommender Systems (RecSys).

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(2020). Data-Driven Off-Policy Estimator Selection: An Application in User Marketing on An Online Content Delivery Service. RecSys 2020 Workshop on Bandit and Reinforcement Learning from User Interactions.

引用 arXiv

(2020). Unbiased Pairwise Learning from Biased Implicit Feedback. In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval (ICTIR).

引用 コード 会議録

(2020). Unbiased lift-based bidding system. AdKDD & TargetAd 2020 Workshop (held in conjunction with KDD2020).

引用 arXiv

(2020). Dual Learning Algorithm for Delayed Conversions. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR).

引用 スライド 会議録

(2020). Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models. In Proceedings of 37th International Conference on Machine Learning (ICML).

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(2020). Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR).

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(2020). すべての機械学習は A/B テストである. 人工知能, Vol.35, No.4.

学会誌

(2020). Cost-Effective and Stable Policy Optimization Algorithm for Uplift Modeling with Multiple Treatments. In Proceedings of the 2020 SIAM International Conference on Data Mining (SDM).

引用 会議録

(2020). Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback. In Proceedings of the 13th International Conference on Web Search and Data Mining (WSDM).

引用 コード スライド 会議録

(2019). Doubly Robust Prediction and Evaluation Methods Improve Uplift Modeling for Observational Data. In Proceedings of the 2019 SIAM International Conference on Data Mining (SDM).

引用 会議録