wasserstein gradient flow
Particle-based Variational Inference with Generalized Wasserstein Gradient Flow
We show that GWG exhibits strong convergence guarantees.
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Mean-Field Langevin Dynamics for Signed Measures via a Bilevel Approach Guillaume Wang
In this paper, we investigate how to extend the MFLD framework to convex optimization problems over signed measures.
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Wasserstein Gradient Flows for Optimizing Gaussian Mixture Policies Hanna Ziesche and Leonel Rozo
Robots often rely on a repertoire of previously-learned motion policies for performing tasks of diverse complexities.
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Global Convergence in Training Large-Scale Transformers
Despite the widespread success of Transformers across various domains, their optimization guarantees in large-scale model settings are not well-understood.
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Variational inference via Wasserstein gradient flows
Below, we proceed to describe our contributions in greater detail. Our framework builds upon the seminal work of Jordan et al. [1998], which introduced the celebrated
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