9 citations · 18 across the 7 of their papers we have counts for
7 papers
Private Learning with Public Feature Conditioning
Shuli Jiang, Walid Krichene, Nicolas Mayoraz
We study differentially private (DP) regression in settings where each data sample includes public, non-sensitive features -- common in applications such as recommendation and adve…
Efficient Hyperparameter Search for Non-Stationary Model Training
Berivan Isik, Matthew Fahrbach, Dima Kuzmin +4
Online learning is the cornerstone of applications like recommendation and advertising systems, where models continuously adapt to shifting data distributions. Model training for s…
Successive Halving with Learning Curve Prediction via Latent Kronecker Gaussian Processes
Jihao Andreas Lin, Nicolas Mayoraz, Steffen Rendle +3
Successive Halving is a popular algorithm for hyperparameter optimization which allocates exponentially more resources to promising candidates. However, the algorithm typically rel…
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…
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
Martin Mladenov, Chih-Wei Hsu, Vihan Jain +7
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…
Scaling Up Collaborative Filtering Data Sets through Randomized Fractal Expansions
Francois Belletti, Karthik Lakshmanan, Walid Krichene +7
Recommender system research suffers from a disconnect between the size of academic data sets and the scale of industrial production systems. In order to bridge that gap, we propose…
Efficient Training on Very Large Corpora via Gramian Estimation
Walid Krichene, Nicolas Mayoraz, Steffen Rendle +5
We study the problem of learning similarity functions over very large corpora using neural network embedding models. These models are typically trained using SGD with sampling of r…