Publications

Preprints

  1. Yuta Saito, Shunsuke Aihara, Megumi Matsutani, and Yusuke Narita.
    Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation
    arXiv preprint arXiv:2008.07146, 2020.
    [arXiv] [software] [public dataset]

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] [proposal]

International Conference Proceedings (refereed)

  1. 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]

  2. 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]

  3. 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]

  4. 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]

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

  6. 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]

  7. 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]

  8. 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]

  9. 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]

  10. 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]

  11. 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.
    Potential Use of Simulation: Accerelating Offline Reinforcement Learning.
    RecSys 2021 Workshop on Simulation Methods for Recommender Systems (SimuRec), 2021.

  2. 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
    ICML 2021 Workshop on Reinforcement Learning for Real Life (RL4RealLife), 2021.

  3. 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.

  4. 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).
    [arXiv] [software] [dataset] [slides]

  5. 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]

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