All Stories

  1. Additive Control Variates Dominate Self-Normalisation in Off-Policy Evaluation
  2. Unifying On- and Off-Policy Variance Reduction Methods
  3. Variance Reduction for Heavy-Tailed Monetization Metrics in Ranking Experiments via Post-Stratification
  4. Optimal Baseline Corrections for Off-Policy Contextual Bandits
  5. Δ-OPE: Off-Policy Estimation with Pairs of Policies
  6. CONSEQUENCES --- The 3rd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems
  7. Multi-Objective Recommendation via Multivariate Policy Learning
  8. Powerful A/B-Testing Metrics and Where to Find Them
  9. Learning Metrics that Maximise Power for Accelerated A/B-Tests
  10. On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top- n Recommendation
  11. Monitoring the Evolution of Behavioural Embeddings in Social Media Recommendation
  12. Practical Bandits: An Industry Perspective
  13. Ad-load Balancing via Off-policy Learning in a Content Marketplace
  14. On Gradient Boosted Decision Trees and Neural Rankers: A Case-Study on Short-Video Recommendations at ShareChat
  15. CONSEQUENCES — The 2nd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems
  16. A Probabilistic Position Bias Model for Short-Video Recommendation Feeds
  17. A Common Misassumption in Online Experiments with Machine Learning Models
  18. Tutorials at The Web Conference 2023
  19. Pessimistic Decision-Making for Recommender Systems
  20. CONSEQUENCES — Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems
  21. Pessimistic Reward Models for Off-Policy Learning in Recommendation
  22. Top-K Contextual Bandits with Equity of Exposure
  23. Closed-Form Models for Collaborative Filtering with Side-Information
  24. Joint Policy-Value Learning for Recommendation
  25. A Gentle Introduction to Recommendation as Counterfactual Policy Learning