no code implementations • 24 Oct 2023 • Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang
We study a class of private learning problems in which the data is a join of private and public features.
1 code implementation • 14 Mar 2021 • Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier
The development of recommender systems that optimize multi-turn interaction with users, and model the interactions of different agents (e. g., users, content providers, vendors) in the recommender ecosystem have drawn increasing attention in recent years.
no code implementations • NeurIPS 2020 • Weiwei Kong, Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang
Several machine learning models involve mapping a score vector to a probability vector.
no code implementations • 8 Apr 2019 • Francois Belletti, Karthik Lakshmanan, Walid Krichene, Nicolas Mayoraz, Yi-fan Chen, John Anderson, Taylor Robie, Tayo Oguntebi, Dan Shirron, Amit Bleiwess
Recommender system research suffers from a disconnect between the size of academic data sets and the scale of industrial production systems.
no code implementations • ICLR 2019 • Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang, Xinyang Yi, Lichan Hong, Ed Chi, John Anderson
We study the problem of learning similarity functions over very large corpora using neural network embedding models.