971 citations · 3k across the 74 of their papers we have counts for
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Squared Neural Families: A New Class of Tractable Density Models
Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic
Flexible models for probability distributions are an essential ingredient in many machine learning tasks. We develop and investigate a new class of probability distributions, which…
Deconditional Downscaling with Gaussian Processes
Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic
Refining low-resolution (LR) spatial fields with high-resolution (HR) information, often known as statistical downscaling, is challenging as the diversity of spatial datasets often…
A Perspective on Gaussian Processes for Earth Observation
Gustau Camps-Valls, Dino Sejdinovic, Jakob Runge +1
Earth observation (EO) by airborne and satellite remote sensing and in-situ observations play a fundamental role in monitoring our planet. In the last decade, machine learning and…
Hamiltonian Variational Auto-Encoder
Anthony L. Caterini, Arnaud Doucet, Dino Sejdinovic
Variational Auto-Encoders (VAEs) have become very popular techniques to perform inference and learning in latent variable models as they allow us to leverage the rich representatio…
Causal Inference via Kernel Deviance Measures
Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
Discovering the causal structure among a set of variables is a fundamental problem in many areas of science. In this paper, we propose Kernel Conditional Deviance for Causal Infere…
Hypothesis testing using pairwise distances and associated kernels (with Appendix)
Dino Sejdinovic, Arthur Gretton, Bharath Sriperumbudur +1
We provide a unifying framework linking two classes of statistics used in two-sample and independence testing: on the one hand, the energy distances and distance covariances from t…