3 accepted papers
As causal ground truth is incredibly rare, causal discovery algorithms are commonly only evaluated on simulated data. This is concerning, given that simulations reflect preconceptions about generating processes regarding noise distributions, model classes, and more. In this work, we propose a novel…
Despite the ubiquity of kernel-based clustering, surprisingly few statistical guarantees exist beyond settings that consider strong structural assumptions on the data generation process. In this work, we take a step towards bridging this gap by studying the statistical performance of kernel-based cl…
This paper studies the optimality of kernel methods in high dimensional data clustering. Recent works have studied the large sample performance of kernel clustering in the high dimensional regime, where Euclidean distance becomes less informative. However, it is unknown whether popular methods, such…