157 citations · 192 across the 5 of their papers we have counts for
3 papers · 1 filter
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin +6
ML models are ubiquitous in real world applications and are a constant focus of research. At the same time, the community has started to realize the importance of protecting the pr…
Temporal Multimodal Multivariate Learning
Hyoshin Park, Justice Darko, Niharika Deshpande +7
We introduce temporal multimodal multivariate learning, a new family of decision making models that can indirectly learn and transfer online information from simultaneous observati…
Toward Training at ImageNet Scale with Differential Privacy
Alexey Kurakin, Shuang Song, Steve Chien +3
Differential privacy (DP) is the de facto standard for training machine learning (ML) models, including neural networks, while ensuring the privacy of individual examples in the tr…