All Stories

  1. Efficient Re-ranking with Cross-encoders via Early Exit
  2. Explainable, Effective, and Efficient Learning-to-Rank Models Using ILMART
  3. Early Exit Strategies for Approximate k -NN Search in Dense Retrieval
  4. A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor Search
  5. Special Section on Efficiency in Neural Information Retrieval
  6. LambdaRank Gradients are Incoherent
  7. Can Embeddings Analysis Explain Large Language Model Ranking?
  8. On the Effect of Low-Ranked Documents: A New Sampling Function for Selective Gradient Boosting
  9. Filtering out Outliers in Learning to Rank
  10. ReNeuIR: Reaching Efficiency in Neural Information Retrieval
  11. ILMART: Interpretable Ranking with Constrained LambdaMART
  12. EiFFFeL
  13. A comparison of spatio-temporal prediction methods
  14. Learning Early Exit Strategies for Additive Ranking Ensembles
  15. Adaptive attacks on machine learning models
  16. Query-level Early Exit for Additive Learning-to-Rank Ensembles
  17. Adversarial Training of Gradient-Boosted Decision Trees
  18. Learning to Rank in Theory and Practice
  19. X-CLE a VER
  20. Efficient and Effective Query Expansion for Web Search
  21. Continuation Methods and Curriculum Learning for Learning to Rank
  22. Selective Gradient Boosting for Effective Learning to Rank
  23. Do Violent People Smile