48 citations · 61 across the 5 of their papers we have counts for
5 papers
Recent Advances in the Foundations and Applications of Unbiased Learning to Rank
Shashank Gupta, Philipp Hager, Jin Huang +2
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…
On the Impact of Outlier Bias on User Clicks
Fatemeh Sarvi, Ali Vardasbi, Mohammad Aliannejadi +2
User interaction data is an important source of supervision in counterfactual learning to rank (CLTR). Such data suffers from presentation bias. Much work in unbiased learning to r…
State Spaces Aren't Enough: Machine Translation Needs Attention
Ali Vardasbi, Telmo Pessoa Pires, Robin M. Schmidt +1
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. Thanks to…
Intersection of Parallels as an Early Stopping Criterion
Ali Vardasbi, Maarten de Rijke, Mostafa Dehghani
A common way to avoid overfitting in supervised learning is early stopping, where a held-out set is used for iterative evaluation during training to find a sweet spot in the number…
When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank
Ali Vardasbi, Harrie Oosterhuis, Maarten de Rijke
Besides position bias, which has been well-studied, trust bias is another type of bias prevalent in user interactions with rankings: users are more likely to click incorrectly w.r.…