Anna Korba - Kernel Stein Discrepancy Descent
Presentation given by Anna Korba on 20th October in the one world seminar on the mathematics of machine learning on the topic "Kernel Stein Discrepancy Descent".
Abstract: Among dissimilarities between probability distributions, the Kernel Stein Discrepancy (KSD) has received much interest recently. We investigate the properties of its Wasserstein gradient flow to approximate a target probability distribution known up to a normalization constant. This leads to a straightforwardly implementable, deterministic score-based method, named KSD Descent, which uses a set of particles to approximate the target distribution. Remarkably, owing to a tractable loss function, KSD Descent can leverage robust parameter-free optimization schemes such as L-BFGS; this contrasts with other popular particle-based schemes such as the Stein Variational Gradient Descent algorithm. We study the convergence properties of KSD Descent and demonstrate its practical relevance. However, we also highlight failure cases by showing that the algorithm can get stuck in spurious local minima.
Видео Anna Korba - Kernel Stein Discrepancy Descent канала One world theoretical machine learning
Abstract: Among dissimilarities between probability distributions, the Kernel Stein Discrepancy (KSD) has received much interest recently. We investigate the properties of its Wasserstein gradient flow to approximate a target probability distribution known up to a normalization constant. This leads to a straightforwardly implementable, deterministic score-based method, named KSD Descent, which uses a set of particles to approximate the target distribution. Remarkably, owing to a tractable loss function, KSD Descent can leverage robust parameter-free optimization schemes such as L-BFGS; this contrasts with other popular particle-based schemes such as the Stein Variational Gradient Descent algorithm. We study the convergence properties of KSD Descent and demonstrate its practical relevance. However, we also highlight failure cases by showing that the algorithm can get stuck in spurious local minima.
Видео Anna Korba - Kernel Stein Discrepancy Descent канала One world theoretical machine learning
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22 октября 2021 г. 17:27:31
00:51:06
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