dbmml.github.io - Distance-based methods in Machine Learning

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🔊 New & improved venue: Bentham House About Programme Speakers Organisers Location Distance-based methods represent a varied and extensively used set of techniques for performing statistical learning by minimising the distance or discrepancy between probability distributions. One key advantage of distance-based techniques is that the resulting model's properties are dependent on the underlying distance selected. Crafting distances that encode desirable properties, such as stability and robustness, is a prom

The workshop on will cover a broad range of statistical and machine learning methods, including but not limited to parameter estimation, generalised Bayes, hypothesis testing, optimal transport, which are based on statistical distances such as the Maximum Mean Discrepancy (MMD), Kernel Stein Discrepancy (KSD), score matching, Wasserstein distances, Sinkhorn divergences, Kullback-Leibler (KL) divergence, and more.

University of Cambridge

Links to dbmml.github.io (3)

mashanaslidnyk.github.io Masha Naslidnyk
maltamiranomontero.github.io Matías Altamirano
martacatalano.github.io Marta Catalano