4 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.IR2022
Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance
Aijun Bai, Rolf Jagerman, Zhen Qin +6
As Learning-to-Rank (LTR) approaches primarily seek to improve ranking quality, their output scores are not scale-calibrated by design. This fundamentally limits LTR usage in score…
cs.CR2013★ 4 cited
On the Benefits of Sampling in Privacy Preserving Statistical Analysis on Distributed Databases
Bing-Rong Lin, Ye Wang, Shantanu Rane
We consider a problem where mutually untrusting curators possess portions of a vertically partitioned database containing information about a set of individuals. The goal is to ena…
cs.DB2012★ 2 cited
A Framework for Extracting Semantic Guarantees from Privacy
Bing-Rong Lin, Daniel Kifer
Statistical privacy views privacy definitions as contracts that guide the behavior of algorithms that take in sensitive data and produce sanitized data. For most existing privacy d…