Introduces Generative Privacy Funnel (GenPF) and deep variational PF (DVPF) models that extend the privacy funnel to generative settings and provide a controllable privacy-utility trade-off with reduced sensitive attribute leakage in face recognition.
Computational optimal transport: With applications to data science
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Introduces a robust OT divergence with stochastic subgradient algorithm and bootstrap-based SBI procedure for parameter inference under joint geometric and TV contamination.
Flow Matching is a generative modeling framework with mathematical foundations, design choices, extensions, and open-source PyTorch code for applications like image and text generation.
citing papers explorer
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Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition
Introduces Generative Privacy Funnel (GenPF) and deep variational PF (DVPF) models that extend the privacy funnel to generative settings and provide a controllable privacy-utility trade-off with reduced sensitive attribute leakage in face recognition.
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Robust Simulation Based Inference Through Robust Optimal Transport
Introduces a robust OT divergence with stochastic subgradient algorithm and bootstrap-based SBI procedure for parameter inference under joint geometric and TV contamination.
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Flow Matching Guide and Code
Flow Matching is a generative modeling framework with mathematical foundations, design choices, extensions, and open-source PyTorch code for applications like image and text generation.