activity
20102023
most citedPrivacy via the Johnson-Lindenstrauss Transform

106 citations · 115 across the 5 of their papers we have counts for

collaborators

5 papers

cs.DS2023

Fast Private Kernel Density Estimation via Locality Sensitive Quantization

Tal Wagner, Yonatan Naamad, Nina Mishra

We study efficient mechanisms for differentially private kernel density estimation (DP-KDE). Prior work for the Gaussian kernel described algorithms that run in time exponential in…

cs.LG2023★ 1 cited

Explaining a machine learning decision to physicians via counterfactuals

Supriya Nagesh, Nina Mishra, Yonatan Naamad +3

Machine learning models perform well on several healthcare tasks and can help reduce the burden on the healthcare system. However, the lack of explainability is a major roadblock t…

cs.LG2012★ 5 cited

Predicting Preference Flips in Commerce Search

Or Sheffet, Nina Mishra, Samuel Ieong

Traditional approaches to ranking in web search follow the paradigm of rank-by-score: a learned function gives each query-URL combination an absolute score and URLs are ranked acco…

cs.DS2012★ 106 cited

Privacy via the Johnson-Lindenstrauss Transform

Krishnaram Kenthapadi, Aleksandra Korolova, Ilya Mironov +1

Suppose that party A collects private information about its users, where each user's data is represented as a bit vector. Suppose that party B has a proprietary data mining algorit…

cs.LG2010★ 3 cited

Adapting to the Shifting Intent of Search Queries

Umar Syed, Aleksandrs Slivkins, Nina Mishra

Search engines today present results that are often oblivious to abrupt shifts in intent. For example, the query `independence day' usually refers to a US holiday, but the intent o…