1 citations · 1 across the 6 of their papers we have counts for
7 papers
Measuring Differences between Conditional Distributions using Kernel Embeddings
Peter Moskvichev, Siu Lun Chau, Dino Sejdinovic
Comparing conditional distributions is a fundamental challenge in statistics and machine learning, with applications across a wide range of domains. While proposed methods for meas…
The Generalised Kernel Covariance Measure
Luca Bergen, Dino Sejdinovic, Vanessa Didelez
We consider the problem of conditional independence (CI) testing and adopt a kernel-based approach. Kernel-based CI tests embed variables in reproducing kernel Hilbert spaces, regr…
Gaussian Processes and Reproducing Kernel Hilbert Spaces: Connections and Equivalences
Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic +1
This monograph studies the relations between two approaches using positive definite kernels: probabilistic methods using Gaussian processes, and non-probabilistic methods using rep…
All Models Are Miscalibrated, But Some Less So: Comparing Calibration with Conditional Mean Operators
Peter Moskvichev, Dino Sejdinovic
When working in a high-risk setting, having well calibrated probabilistic predictive models is a crucial requirement. However, estimators for calibration error are not always able…
An Overview of Causal Inference using Kernel Embeddings
Dino Sejdinovic
Kernel embeddings have emerged as a powerful tool for representing probability measures in a variety of statistical inference problems. By mapping probability measures into a repro…
Neural-Kernel Conditional Mean Embeddings
Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic
Kernel conditional mean embeddings (CMEs) offer a powerful framework for representing conditional distribution, but they often face scalability and expressiveness challenges. In th…
Hyperspectral Image Classification with Support Vector Machines on Kernel Distribution Embeddings
Gianni Franchi, Jesus Angulo, Dino Sejdinovic
We propose a novel approach for pixel classification in hyperspectral images, leveraging on both the spatial and spectral information in the data. The introduced method relies on a…