Publications

Tutorials

1. Yuta Saito and Thorsten Joachims.
   Counterfactual Evaluation and Learning for Recommender Systems:
   Foundations, Implementations, and Recent Advances
   ACM Conference on Recommender Systems (RecSys2021).
   [website] [GitHub] [proposal]

International Conference Proceedings (refereed)

1. Haruka Kiyohara, Yuta Saito, Tatsuya Matsuhiro, Yusuke Narita, Nobuyuki Shimizu, and Yasuo Yamamoto.
   Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model
   International Conference on Web Search and Data Mining (WSDM2022). (Acceptance rate=20.2%)
   [paper] [code] [slides]
   

2. Yuta Saito, Shunsuke Aihara, Megumi Matsutani, and Yusuke Narita.
   Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation
   Neural Information Processing Systems (NeurIPS2021) Datasets and Benchmarks Track.
   [paper] [software] [public dataset]
   

3. Daisuke Moriwaki, Yuta Hayakawa, Isshu Munemasa, Yuta Saito, and Akira Matsui.
   A Real-World Implementation of UnbiasedLift-based Bidding System
   Proceedings of the 2021 IEEE International Conference on Big Data (BigData2021).
   

4. Masahiro Nomura* and Yuta Saito* (*equal contribution).
   Efficient Hyperparameter Optimization under Multi-Source Covariate Shift
   ACM International Conference on Information and Knowledge Management (CIKM2021). (Acceptance rate=21.7%)
   [paper] [code]

5. Yuta Saito*, Takuma Udagawa*, Haruka Kiyohara, Kazuki Mogi, Yusuke Narita, and Kei Tateno. (*equal contribution)
   Evaluating the Robustness of Off-Policy Evaluation
   ACM Conference on Recommender Systems (RecSys2021). (Acceptance rate=18.4%)
   [paper] [package] [slides]
   

6. Nathan Kallus, Yuta Saito, and Masatoshi Uehara.
   Optimal Off-Policy Evaluation from Multiple Logging Policies
   International Conference on Machine Learning (ICML2021). (Acceptance rate=21.5%)
   [paper] [code]

7. Yuta Saito.
   Doubly Robust Estimator for Ranking Metrics with Post-Click Conversions
   ACM Conference on Recommender Systems (RecSys2020). (Acceptance rate=17.9%)
   [paper] [code] [slides]
   

8. Yuta Saito.
   Unbiased Pairwise Learning from Biased Implicit Feedback
   International Conference on the Theory of Information Retrieval (ICTIR2020). (Acceptance rate=40.5%)
   [paper] [code]
   

9. Yuta Saito and Shota Yasui.
   Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models
   International Conference on Machine Learning (ICML2020). (Acceptance rate=21.8%)
   [paper] [code] [slides]
   

10. Yuta Saito.
    Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback
    International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR2020). (Acceptance rate=26.5%)
    [paper] [code] [slides]
    

11. Yuta Saito, Gota Morishita, and Shota Yasui.
    Dual Learning Algorithm for Delayed Conversions
    International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR2020). (Acceptance rate of short paper=30.0%)
    [paper] [slides]
    

12. Yuta Saito, Hayato Sakata, and Kazuhide Nakata.
    Cost-Effective and Stable Policy Optimization Algorithm for Uplift Modeling with Multiple Treatments
    SIAM International Conference on Data Mining (SDM2020). (Acceptance rate=24.0%)
    [paper] [supplementary material]
    

13. Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata.
    Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback
    International Conference on Web Search and Data Mining (WSDM2020). (Acceptance rate=14.8%)
    [paper] [code] [slides]
    

14. Yuta Saito, Hayato Sakata, and Kazuhide Nakata.
    Doubly Robust Prediction and Evaluation Methods Improve Uplift Modeling for Observational Data
    SIAM International Conference on Data Mining (SDM2019). (Acceptance rate=22.7%)
    [paper] [supplementary material]
    

Workshop Papers (refereed)

1. Haruka Kiyohara, Kosuke Kawakami, and Yuta Saito.
   Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation.
   RecSys 2021 Workshop on Simulation Methods for Recommender Systems (SimuRec), 2021.

2. Yuta Saito, Takuma Udagawa, and Kei Tateno.
   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 (REVEAL2020), 2020.

3. Yuta Saito, Shunsuke Aihara, Megumi Matsutani, and Yusuke Narita.
   A Large-scale Open Dataset for Bandit Algorithms
   ICML 2020 Workshop on Real World Experiment Design and Active Learning (RealML2020).
   RecSys 2020 Workshop on Bandit and Reinforcement Learning from User Interactions (REVEAL2020), 2020 (oral presentation).

4. Daisuke Moriwaki, Yuta Hayakawa, Isshu Munemasa, Yuta Saito, and Akira Matsui.
   Unbiased Lift-based Bidding System
   AdKDD & TargetAd 2020 Workshop (held in conjunction with KDD2020) (AdKDD2020).
   [arXiv]

5. Yuta Saito.
   Offline Recommender Learning Meets Unsupervised Domain Adaptation
   The forum for newcomers to ML (held in conjunction with NeurIPS2019) (NewInML2019).
   [arXiv]