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17 papers · 2 filters
Practical methods for graph two-sample testing
Debarghya Ghoshdastidar, Ulrike von Luxburg
Hypothesis testing for graphs has been an important tool in applied research fields for more than two decades, and still remains a challenging problem as one often needs to draw in…
Foundations of Comparison-Based Hierarchical Clustering
Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg
We address the classical problem of hierarchical clustering, but in a framework where one does not have access to a representation of the objects or their pairwise similarities. In…
Informative Features for Model Comparison
Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy +3
Given two candidate models, and a set of target observations, we address the problem of measuring the relative goodness of fit of the two models. We propose two new statistical tes…
Learning stable and predictive structures in kinetic systems: Benefits of a causal approach
Niklas Pfister, Stefan Bauer, Jonas Peters
Learning kinetic systems from data is one of the core challenges in many fields. Identifying stable models is essential for the generalization capabilities of data-driven inference…
Resampled Priors for Variational Autoencoders
Matthias Bauer, Andriy Mnih
We propose Learned Accept/Reject Sampling (LARS), a method for constructing richer priors using rejection sampling with a learned acceptance function. This work is motivated by rec…
Comparison-Based Random Forests
Siavash Haghiri, Damien Garreau, Ulrike von Luxburg
Assume we are given a set of items from a general metric space, but we neither have access to the representation of the data nor to the distances between data points. Instead, supp…