no code implementations • 26 Jan 2024 • Girish Kumar, Thomas Strohmer, Roman Vershynin
Much of the research in differential privacy has focused on offline applications with the assumption that all data is available at once.
1 code implementation • 25 Oct 2022 • Xiang Yue, Huseyin A. Inan, Xuechen Li, Girish Kumar, Julia McAnallen, Hoda Shajari, Huan Sun, David Levitan, Robert Sim
Privacy concerns have attracted increasing attention in data-driven products due to the tendency of machine learning models to memorize sensitive training data.
1 code implementation • ACL 2021 • Parker Riley, Noah Constant, Mandy Guo, Girish Kumar, David Uthus, Zarana Parekh
Unlike previous approaches requiring style-labeled training data, our method makes use of readily-available unlabeled text by relying on the implicit connection in style between adjacent sentences, and uses labeled data only at inference time.
no code implementations • 28 Sep 2020 • Parker Riley, Noah Constant, Mandy Guo, Girish Kumar, David Uthus, Zarana Parekh
We present a novel approach to the challenging problem of label-free text style transfer.
3 code implementations • WS 2019 • Matthew Henderson, Paweł Budzianowski, Iñigo Casanueva, Sam Coope, Daniela Gerz, Girish Kumar, Nikola Mrkšić, Georgios Spithourakis, Pei-Hao Su, Ivan Vulić, Tsung-Hsien Wen
Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches.
BIG-bench Machine Learning Conversational Response Selection +1
no code implementations • 6 Feb 2018 • Girish Kumar, Matthew Henderson, Shannon Chan, Hoang Nguyen, Lucas Ngoo
Sellers in user to user marketplaces can be inundated with questions from potential buyers.