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Mathematik des Maschinellen Lernens

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

Differentially Private Gradient Flow for Generative Modeling
Datum: 30.04.2024, 16:15 - 17:15 Uhr
Kategorie: Veranstaltung
Ort: Hubland Süd, Geb. Z6 (Zentrales Hörsaal- u. Seminargebäude), 0.002
Veranstalter: Center for Artificial Intelligence and Data Science (CAIDAS)
Vortragende: Dr. Muni Sreenivas Pydi, Universite PSL, Paris
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.

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