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

arXiv: https://arxiv.org/abs/2305.02914 · HTML: https://mlsanity.com/paper/2305.02914/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 59 references that are in the map.
- First posted on arXiv 4 May 2023.

## Builds on (most cited first, 27 of 27)

- [Doubly Robust Off-policy Value Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1511.03722/) (2015)
- [Fairness of Exposure in Rankings](https://mlsanity.com/paper/1802.07281/) (2018)
- [Unbiased Learning-to-Rank with Biased Feedback](https://mlsanity.com/paper/1608.04468/) (2016)
- [Equity of Attention: Amortizing Individual Fairness in Rankings](https://mlsanity.com/paper/1805.01788/) (2018)
- [Policy Learning for Fairness in Ranking](https://mlsanity.com/paper/1902.04056/) (2019)
- [Controlling Fairness and Bias in Dynamic Learning-to-Rank](https://mlsanity.com/paper/2005.14713/) (2020)
- [Off-Policy Evaluation for Large Action Spaces via Embeddings](https://mlsanity.com/paper/2202.06317/) (2022)
- [Correcting for Selection Bias in Learning-to-rank Systems](https://mlsanity.com/paper/2001.11358/) (2020)
- [Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model](https://mlsanity.com/paper/2202.01562/) (2022)
- [Fairness in Ranking under Uncertainty](https://mlsanity.com/paper/2107.06720/) (2021)
- [Differentiable Unbiased Online Learning to Rank](https://mlsanity.com/paper/1809.08415/) (2018)
- [Doubly-Robust Estimation for Correcting Position-Bias in Click Feedback for Unbiased Learning to Rank](https://mlsanity.com/paper/2203.17118/) (2022)
- [From Importance Sampling to Doubly Robust Policy Gradient](https://mlsanity.com/paper/1910.09066/) (2019)
- [Policy-Gradient Training of Fair and Unbiased Ranking Functions](https://mlsanity.com/paper/1911.08054/) (2019)
- [Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank](https://mlsanity.com/paper/2005.11938/) (2020)
- [Unifying Online and Counterfactual Learning to Rank](https://mlsanity.com/paper/2012.04426/) (2020)
- [Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization](https://mlsanity.com/paper/2305.01522/) (2023)
- [Maximizing Marginal Fairness for Dynamic Learning to Rank](https://mlsanity.com/paper/2102.09670/) (2021)
- [Reaching the End of Unbiasedness: Uncovering Implicit Limitations of Click-Based Learning to Rank](https://mlsanity.com/paper/2206.12204/) (2022)
- [Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking](https://mlsanity.com/paper/2007.12719/) (2020)
- [Understanding and Mitigating the Effect of Outliers in Fair Ranking](https://mlsanity.com/paper/2112.11251/) (2021)
- [Learning from User Interactions with Rankings: A Unification of the Field](https://mlsanity.com/paper/2012.06576/) (2020)
- [Counterfactual Learning to Rank using Heterogeneous Treatment Effect Estimation](https://mlsanity.com/paper/2007.09798/) (2020)
- [Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking](https://mlsanity.com/paper/2204.13765/) (2022)
- [Unbiased Top-k Learning to Rank with Causal Likelihood Decomposition](https://mlsanity.com/paper/2204.00815/) (2022)
- [Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion](https://mlsanity.com/paper/2204.10936/) (2022)
- [Unbiased Learning to Rank: Counterfactual and Online Approaches](https://mlsanity.com/paper/1907.07260/) (2019)

## Built on by (most cited first, 1 of 1)

- [Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation](https://mlsanity.com/paper/2608.05819/) (2026)

## Abstract (arXiv)

Since its inception, the field of unbiased learning to rank (ULTR) has remained very active and has seen several impactful advancements in recent years. This tutorial provides both an introduction to the core concepts of the field and an overview of recent advancements in its foundations along with several applications of its methods. The tutorial is divided into four parts: Firstly, we give an overview of the different forms of bias that can be addressed with ULTR methods. Secondly, we present a comprehensive discussion of the latest estimation techniques in the ULTR field. Thirdly, we survey published results of ULTR in real-world applications. Fourthly, we discuss the connection between ULTR and fairness in ranking. We end by briefly reflecting on the future of ULTR research and its applications. This tutorial is intended to benefit both researchers and industry practitioners who are interested in developing new ULTR solutions or utilizing them in real-world applications.