Vahab Mirrokni

This article discusses using aggregate data in machine learning while protecting privacy.

A look into learning from group responses while ensuring data privacy.

Optimizing data selection can significantly enhance machine learning training efficiency and performance.

Strategies to enhance efficiency in data sharing across multiple servers.

Exploring how transformers tackle various graph reasoning tasks effectively.

Learn how to create well-conditioned bases using different methods for data representation.

A study on preserving privacy while analyzing sensitive data using differential privacy techniques.

LLP enables model training using average labels from grouped examples.

This article discusses retraining methods using model predictions for improved accuracy.