Harrie Oosterhuis

CoRR, 2024

A Simpler Alternative to Variational Regularized Counterfactual Risk Minimization.

[BibT_eX]

[DOI]

Hua Chang Bakker

CoRR, 2024

A First Look at Selection Bias in Preference Elicitation for Recommendation.

[BibT_eX]

[DOI]

CoRR, 2024

Unbiased Learning to Rank: On Recent Advances and Practical Applications.

[BibT_eX]

[DOI]

Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024

Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted Trees.

[BibT_eX]

[DOI]

Jingwei Kang

Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024

Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems.

[BibT_eX]

[DOI]

Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024

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

[BibT_eX]

[DOI]

Proceedings of the 18th ACM Conference on Recommender Systems, 2024

Optimal Baseline Corrections for Off-Policy Contextual Bandits.

[BibT_eX]

[DOI]

Proceedings of the 18th ACM Conference on Recommender Systems, 2024

Reliable Confidence Intervals for Information Retrieval Evaluation Using Generative A.I.

[BibT_eX]

[DOI]

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions.

[BibT_eX]

[DOI]

Lijun Lyu

Avishek Anand

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing.

[BibT_eX]

[DOI]

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?

[BibT_eX]

[DOI]

Proceedings of the Advances in Information Retrieval, 2024

Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank.

[BibT_eX]

[DOI]

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

2023

Doubly Robust Estimation for Correcting Position Bias in Click Feedback for Unbiased Learning to Rank.

[BibT_eX]

[DOI]

ACM Trans. Inf. Syst., 2023

Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization.

[BibT_eX]

[DOI]

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

Recent Advances in the Foundations and Applications of Unbiased Learning to Rank.

[BibT_eX]

[DOI]

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta Distributions.

[BibT_eX]

[DOI]

Norman Knyazev

Proceedings of the 17th ACM Conference on Recommender Systems, 2023

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

[BibT_eX]

[DOI]

Proceedings of the 17th ACM Conference on Recommender Systems, 2023

A Deep Generative Recommendation Method for Unbiased Learning from Implicit Feedback.

[BibT_eX]

[DOI]

Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval, 2023

Recent Advancements in Unbiased Learning to Rank.

[BibT_eX]

[DOI]

Philipp K. Hager

Proceedings of the 15th Annual Meeting of the Forum for Information Retrieval Evaluation, 2023

2022

Doubly-Robust Estimation for Unbiased Learning-to-Rank from Position-Biased Click Feedback.

[BibT_eX]

[DOI]

CoRR, 2022

It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic.

[BibT_eX]

[DOI]

Jin Huang

Proceedings of the WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21, 2022

Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation with Minimal Computational Complexity.

[BibT_eX]

[DOI]

Proceedings of the SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11, 2022

State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study.

[BibT_eX]

[DOI]

Proceedings of the SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11, 2022

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

[BibT_eX]

[DOI]

Proceedings of the RecSys '22: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, September 18, 2022

Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness (Extended Abstract).

[BibT_eX]

[DOI]

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Reaching the End of Unbiasedness: Uncovering Implicit Limitations of Click-Based Learning to Rank.

[BibT_eX]

[DOI]

Proceedings of the ICTIR '22: The 2022 ACM SIGIR International Conference on the Theory of Information Retrieval, Madrid, Spain, July 11, 2022

The Bandwagon Effect: Not Just Another Bias.

[BibT_eX]

[DOI]

Norman Knyazev

Proceedings of the ICTIR '22: The 2022 ACM SIGIR International Conference on the Theory of Information Retrieval, Madrid, Spain, July 11, 2022

Closing the Gender Wage Gap: Adversarial Fairness in Job Recommendation.

[BibT_eX]

[DOI]

Proceedings of the 2nd Workshop on Recommender Systems for Human Resources (RecSys-in-HR 2022) co-located with the 16th ACM Conference on Recommender Systems (RecSys 2022), 2022

FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Robust Generalization and Safe Query-Specialization in Counterfactual Learning to Rank.

[BibT_eX]

[DOI]

CoRR, 2021

Robust Generalization and Safe Query-Specializationin Counterfactual Learning to Rank.

[BibT_eX]

[DOI]

Proceedings of the WWW '21: The Web Conference 2021, 2021

Unifying Online and Counterfactual Learning to Rank: A Novel Counterfactual Estimator that Effectively Utilizes Online Interventions.

[BibT_eX]

[DOI]

Proceedings of the WSDM '21, 2021

Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness.

[BibT_eX]

[DOI]

Proceedings of the SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021

Unifying Online and Counterfactual Learning to Rank: A Novel Counterfactual Estimator that Effectively Utilizes Online Interventions (Extended Abstract).

[BibT_eX]

[DOI]

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Session details: Session 4B - Semantic Retrieval.

[BibT_eX]

[DOI]

Proceedings of the ICTIR '21: The 2021 ACM SIGIR International Conference on the Theory of Information Retrieval, 2021

2020

Learning from user interactions with rankings: a unification of the field.

[BibT_eX]

[DOI]

SIGIR Forum, 2020

Unifying Online and Counterfactual Learning to Rank.

[BibT_eX]

[DOI]

CoRR, 2020

Unbiased Learning to Rank: Counterfactual and Online Approaches.

[BibT_eX]

[DOI]

Rolf Jagerman

Proceedings of the Companion of The 2020 Web Conference 2020, 2020

Policy-Aware Unbiased Learning to Rank for Top-k Rankings.

[BibT_eX]

[DOI]

Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, 2020

Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems.

[BibT_eX]

[DOI]

Proceedings of the RecSys 2020: Fourteenth ACM Conference on Recommender Systems, 2020

Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking.

[BibT_eX]

[DOI]

Proceedings of the ICTIR '20: The 2020 ACM SIGIR International Conference on the Theory of Information Retrieval, 2020

When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank.

[BibT_eX]

[DOI]

Ali Vardasbi

Proceedings of the CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, 2020

2019

Actionable Interpretability through Optimizable Counterfactual Explanations for Tree Ensembles.

[BibT_eX]

[DOI]

CoRR, 2019

Learning to Rank in Theory and Practice: From Gradient Boosting to Neural Networks and Unbiased Learning.

[BibT_eX]

[DOI]

Claudio Lucchese

Franco Maria Nardini

Rama Kumar Pasumarthi

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019

To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions.

[BibT_eX]

[DOI]

Rolf Jagerman

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019

Optimizing Ranking Models in an Online Setting.

[BibT_eX]

[DOI]

Proceedings of the Advances in Information Retrieval, 2019

2018

Optimizing Interactive Systems with Data-Driven Objectives.

[BibT_eX]

[DOI]

CoRR, 2018

Ranking for Relevance and Display Preferences in Complex Presentation Layouts.

[BibT_eX]

[DOI]

Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 2018

Differentiable Unbiased Online Learning to Rank.

[BibT_eX]

[DOI]

Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018

The Potential of Learned Index Structures for Index Compression.

[BibT_eX]

[DOI]

J. Shane Culpepper

Proceedings of the 23rd Australasian Document Computing Symposium, 2018

2017

Query-Level Ranker Specialization.

[BibT_eX]

[DOI]

Rolf Jagerman

Proceedings of the 1st International Workshop on LEARning Next gEneration Rankers co-located with the 3rd ACM International Conference on the Theory of Information Retrieval (ICTIR 2017), 2017

Balancing Speed and Quality in Online Learning to Rank for Information Retrieval.

[BibT_eX]

[DOI]

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017

Sensitive and Scalable Online Evaluation with Theoretical Guarantees.

[BibT_eX]

[DOI]