3 papers
cs.LG2026
CALLIOPE: Private Insights into AI Use, Without Embeddings
Edith Cohen, Vadym Doroshenko, Badih Ghazi +11
We introduce CALLIOPE, a top-down framework for discovering topic hierarchies in large corpora of unstructured data (e.g., LLM conversations), while supporting differential privacy…
cs.LG2026
JAX-Privacy: A library for differentially private machine learning
Ryan McKenna, Galen Andrew, Borja Balle +6
JAX-Privacy is a library designed to simplify the deployment of robust and performant mechanisms for differentially private machine learning. Guided by design principles of usabili…
cs.SE2021
Unsupervised Learning of General-Purpose Embeddings for Code Changes
Mikhail Pravilov, Egor Bogomolov, Yaroslav Golubev +1
Applying machine learning to tasks that operate with code changes requires their numerical representation. In this work, we propose an approach for obtaining such representations d…