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Optimization for ML and AI Seminar: (De)regularized Wasserstein Gradient Flows via Reproducing Kernels

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Optimization for ML and AI Seminar: (De)regularized Wasserstein Gradient Flows via Reproducing Kernels

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February 27

February 27 @ 11:00 am - 12:00 pm

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1 event 27 1 event, 27 11:00 am - 12:00 pm Optimization for ML and AI Seminar: (De)regularized Wasserstein Gradient Flows via Reproducing Kernels February 27 @ 11:00 am - 12:00 pm Optimization for ML and AI Seminar: (De)regularized Wasserstein Gradient Flows via Reproducing Kernels Speaker: Bharath Sriperumbudur, Pennsylvania State University Abstract: Wasserstein gradient flows have become a popular tool in machine learning with applications in sampling, variational inference, generative modeling, and reinforcement learning, among others. The Wasserstein gradient flow (WGF) involves minimizing a probability functional over the Wasserstein space (by taking into account the intrinsic geometry of the Wasserstein space).Continue reading

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