3 citations · 3 across the 1 of their papers we have counts for
4 papers
Submix: Practical Private Prediction for Large-Scale Language Models
Antonio Ginart, Laurens van der Maaten, James Zou +1
Recent data-extraction attacks have exposed that language models can memorize some training samples verbatim. This is a vulnerability that can compromise the privacy of the model's…
Competing AI: How does competition feedback affect machine learning?
Antonio Ginart, Eva Zhang, Yongchan Kwon +1
This papers studies how competition affects machine learning (ML) predictors. As ML becomes more ubiquitous, it is often deployed by companies to compete over customers. For exampl…
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems
Antonio Ginart, Maxim Naumov, Dheevatsa Mudigere +2
Embedding representations power machine intelligence in many applications, including recommendation systems, but they are space intensive -- potentially occupying hundreds of gigab…
Making AI Forget You: Data Deletion in Machine Learning
Antonio Ginart, Melody Y. Guan, Gregory Valiant +1
Intense recent discussions have focused on how to provide individuals with control over when their data can and cannot be used --- the EU's Right To Be Forgotten regulation is an e…