4 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.LG2022★ 4 cited
Toward Understanding Privileged Features Distillation in Learning-to-Rank
Shuo Yang, Sujay Sanghavi, Holakou Rahmanian +2
In learning-to-rank problems, a privileged feature is one that is available during model training, but not available at test time. Such features naturally arise in merchandised rec…
cs.CL2022
PLAtE: A Large-scale Dataset for List Page Web Extraction
Aidan San, Yuan Zhuang, Jan Bakus +6
Recently, neural models have been leveraged to significantly improve the performance of information extraction from semi-structured websites. However, a barrier for continued progr…
cs.LO2021★ 1 cited
Analysis of E-commerce Ranking Signals via Signal Temporal Logic
Tommaso Dreossi, Giorgio Ballardin, Parth Gupta +3
The timed position of documents retrieved by learning to rank models can be seen as signals. Signals carry useful information such as drop or rise of documents over time or user be…