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cs.LG2026
Provably Safe Generative Sampling with Constricting Barrier Functions
Darshan Gadginmath, Ahmed Allibhoy, Fabio Pasqualetti
Flow-based generative models, such as diffusion models and flow matching models, have achieved remarkable success in learning complex data distributions. However, a critical gap re…
cs.LG2023
Noise in the reverse process improves the approximation capabilities of diffusion models
Karthik Elamvazhuthi, Samet Oymak, Fabio Pasqualetti
In Score based Generative Modeling (SGMs), the state-of-the-art in generative modeling, stochastic reverse processes are known to perform better than their deterministic counterpar…
cs.LG2023
Fusing Multiple Algorithms for Heterogeneous Online Learning
Darshan Gadginmath, Shivanshu Tripathi, Fabio Pasqualetti
This study addresses the challenge of online learning in contexts where agents accumulate disparate data, face resource constraints, and use different local algorithms. This paper…