971 citations · 3k across the 74 of their papers we have counts for
8 papers · 1 filter
Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?"
Francois-Xavier Briol, Chris J. Oates, Mark Girolami +2
This article is the rejoinder for the paper "Probabilistic Integration: A Role in Statistical Computation?" to appear in Statistical Science with discussion. We would first like to…
Hyperparameter Learning via Distributional Transfer
Ho Chung Leon Law, Peilin Zhao, Lucian Chan +2
Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial exploration even in cases where similar prior tasks have been solved. We pro…
A Differentially Private Kernel Two-Sample Test
Anant Raj, Ho Chung Leon Law, Dino Sejdinovic +1
Kernel two-sample testing is a useful statistical tool in determining whether data samples arise from different distributions without imposing any parametric assumptions on those d…
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences
Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic +1
This paper is an attempt to bridge the conceptual gaps between researchers working on the two widely used approaches based on positive definite kernels: Bayesian learning or infere…
Towards A Unified Analysis of Random Fourier Features
Zhu Li, Jean-Francois Ton, Dino Oglic +1
Random Fourier features is a widely used, simple, and effective technique for scaling up kernel methods. The existing theoretical analysis of the approach, however, remains focused…
Variational Learning on Aggregate Outputs with Gaussian Processes
Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron +4
While a typical supervised learning framework assumes that the inputs and the outputs are measured at the same levels of granularity, many applications, including global mapping of…
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…