92 citations · 112 across the 11 of their papers we have counts for
4 papers · 1 filter
Private Learning with Public Features
Walid Krichene, Nicolas Mayoraz, Steffen Rendle +3
We study a class of private learning problems in which the data is a join of private and public features. This is often the case in private personalization tasks such as recommenda…
Private Matrix Factorization with Public Item Features
Mihaela Curmei, Walid Krichene, Li Zhang +1
We consider the problem of training private recommendation models with access to public item features. Training with Differential Privacy (DP) offers strong privacy guarantees, at…
Answering Compositional Queries with Set-Theoretic Embeddings
Shib Dasgupta, Andrew McCallum, Steffen Rendle +1
The need to compactly and robustly represent item-attribute relations arises in many important tasks, such as faceted browsing and recommendation systems. A popular machine learnin…
Multi-Task Differential Privacy Under Distribution Skew
Walid Krichene, Prateek Jain, Shuang Song +3
We study the problem of multi-task learning under user-level differential privacy, in which users contribute data to tasks, each involving a subset of users. One important…