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Found 10 papers, 6 papers with code
Group Membership Bias
With group bias, the utility of the sensitive groups is under-estimated, hence, without correcting for this bias, a supposedly fair ranking is not truly fair.
Recent Advances in the Foundations and Applications of Unbiased Learning to Rank
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.
On the Impact of Outlier Bias on User Clicks
We therefore propose an outlier-aware click model that accounts for both outlier and position bias, called outlier-aware position-based model ( OPBM).
State Spaces Aren't Enough: Machine Translation Needs Attention
Structured State Spaces for Sequences (S4) is a recently proposed sequence model with successful applications in various tasks, e. g. vision, language modeling, and audio.
Intersection of Parallels as an Early Stopping Criterion
Using this result, we propose to train two parallel instances of a linear model, initialized with different random seeds, and use their intersection as a signal to detect overfitting.
Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking
Different from PL, where pointwise logits are used as the distribution parameters, in PPG pairwise inversion probabilities together with a reference permutation construct the distribution.
Cross-Market Product Recommendation
We introduce and formalize the problem of cross-market product recommendation, i. e., market adaptation.
Mixture-Based Correction for Position and Trust Bias in Counterfactual Learning to Rank
Affine correction (AC) is a generalization of IPS that corrects for position bias and trust bias.
When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank
Our main contribution is a new estimator based on affine corrections: it both reweights clicks and penalizes items displayed on ranks with high trust bias.
Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank
Unbiased CLTR requires click propensities to compensate for the difference between user clicks and true relevance of search results via IPS.
