Oberseminar "Mathematik des Maschinellen Lernens und Angewandte Analysis" - Dr. Muni Sreenivas Pydi

Safeguarding privacy in sensitive training data is paramount, particularly in the context of generative modeling. This is done through either differentially private stochastic gradient descent, or with a differentially private 
metric for training models or generators. In this talk, I will introduce a novel differentially private generative modeling approach based on parameter-free gradient flows in the space of probability measures. The proposed 
algorithm is a new discretized flow which operates through a particle scheme, utilizing drift derived from the sliced Wasserstein distance and computed in a private manner. Our experiments show that compared to a 
generator-based model, our proposed model can generate higher-fidelity data at a low privacy budget, offering a viable alternative to generator-based approaches.

Der Vortrag findet im Rahmen der AI Talks @ JMU statt.