2016, Data Modeling and Data Silos - Data Science Current

Meet the Final Winners of the U.S. PETs Prize Challenge

DrivenData Labs

MARCH 30, 2023

Our framework involves three key components: (1) model personalization for capturing data heterogeneity across data silos, (2) local noisy gradient descent for silo-specific, node-level differential privacy in contact graphs, and (3) model mean-regularization to balance privacy-heterogeneity trade-offs and minimize the loss of accuracy.

Machine Learning

Machine Learning Machine Learning Data Science Algorithm

Enterprise Generative AI: Take or Shape?

Mlearning.ai

SEPTEMBER 4, 2023

None of these suggestions address congenital defects that result from generative models inexplicably memorizing training data and inadvertently exposing sensitive, copyrighted, or private information. After all, moving a pretrained model is often easier than transferring large datasets. Self-Consuming Generative Models Go MAD.”

AI AI Azure Machine Learning

Data Science Current

Meet the Final Winners of the U.S. PETs Prize Challenge

Enterprise Generative AI: Take or Shape?

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