All Stories

  1. Optimal Baseline Corrections for Off-Policy Contextual Bandits

    Article • October 2024, ACM (Association for Computing Machinery)

    Dr Olivier Jeunen

  2. Δ-OPE: Off-Policy Estimation with Pairs of Policies

    Article • October 2024, ACM (Association for Computing Machinery)

    Dr Olivier Jeunen

  3. CONSEQUENCES --- The 3rd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems

    Article • October 2024, ACM (Association for Computing Machinery)

    Dr Olivier Jeunen, Yixin Wang

  4. Multi-Objective Recommendation via Multivariate Policy Learning

    Article • October 2024, ACM (Association for Computing Machinery)

    Dr Olivier Jeunen

  5. Powerful A/B-Testing Metrics and Where to Find Them

    Article • October 2024, ACM (Association for Computing Machinery)

    Dr Olivier Jeunen

  6. Learning Metrics that Maximise Power for Accelerated A/B-Tests

    Article • August 2024, ACM (Association for Computing Machinery)

    Dr Olivier Jeunen

  7. On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top- n Recommendation

    Article • August 2024, ACM (Association for Computing Machinery)

    Dr Olivier Jeunen

  8. Monitoring the Evolution of Behavioural Embeddings in Social Media Recommendation

    Article • July 2024, ACM (Association for Computing Machinery)

    Dr Olivier Jeunen

  9. Practical Bandits: An Industry Perspective

    Article • March 2024, ACM (Association for Computing Machinery)

    Dr Olivier Jeunen

  10. Ad-load Balancing via Off-policy Learning in a Content Marketplace

    Article • March 2024, ACM (Association for Computing Machinery)

    Hitesh Sagtani, Dr Olivier Jeunen

  11. On Gradient Boosted Decision Trees and Neural Rankers: A Case-Study on Short-Video Recommendations at ShareChat

    Article • December 2023, ACM (Association for Computing Machinery)

    Dr Olivier Jeunen, Hitesh Sagtani

  12. CONSEQUENCES — The 2nd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems

    Article • September 2023, ACM (Association for Computing Machinery)

    Dr Olivier Jeunen

  13. A Probabilistic Position Bias Model for Short-Video Recommendation Feeds

    Article • September 2023, ACM (Association for Computing Machinery)

    Dr Olivier Jeunen

  14. A Common Misassumption in Online Experiments with Machine Learning Models

    Article • ACM SIGIR Forum, June 2023, ACM (Association for Computing Machinery)

    Dr Olivier Jeunen

  15. Tutorials at The Web Conference 2023

    Article • April 2023, ACM (Association for Computing Machinery)

    Dr Olivier Jeunen, Liat Levontin, Josiah Poon, Amit Sheth, Johann Trippas, Dr Shoujin Wang, Jaemin Yoo, Hamed Zamani, Meike Zehlike, Xikun ZHANG, Yushan Jiang, Henry Weld, Haoyang Li, Caren Han, Hande Kucuk McGinty

  16. Pessimistic Decision-Making for Recommender Systems

    Article • ACM Transactions on Recommender Systems, February 2023, ACM (Association for Computing Machinery)

    Dr Olivier Jeunen

  17. CONSEQUENCES — Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems

    Article • September 2022, ACM (Association for Computing Machinery)

    Dr Olivier Jeunen

  18. Pessimistic Reward Models for Off-Policy Learning in Recommendation

    Article • September 2021, ACM (Association for Computing Machinery)

    Dr Olivier Jeunen

  19. Top-K Contextual Bandits with Equity of Exposure

    Article • September 2021, ACM (Association for Computing Machinery)

    Dr Olivier Jeunen

  20. Closed-Form Models for Collaborative Filtering with Side-Information

    Article • September 2020, ACM (Association for Computing Machinery)

    Dr Olivier Jeunen

  21. Joint Policy-Value Learning for Recommendation

    Article • August 2020, ACM (Association for Computing Machinery)

    Dr Olivier Jeunen

  22. A Gentle Introduction to Recommendation as Counterfactual Policy Learning

    Article • July 2020, ACM (Association for Computing Machinery)

    Dr Olivier Jeunen