20 citations · 31 across the 7 of their papers we have counts for
9 papers · 1 filter
Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families
Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic
We introduce squared neural Poisson point processes (SNEPPPs) by parameterising the intensity function by the squared norm of a two layer neural network. When the hidden layer is f…
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…
Scalable Optimal Transport Methods in Machine Learning: A Contemporary Survey
Abdelwahed Khamis, Russell Tsuchida, Mohamed Tarek +2
Optimal Transport (OT) is a mathematical framework that first emerged in the eighteenth century and has led to a plethora of methods for answering many theoretical and applied ques…
Deep equilibrium models as estimators for continuous latent variables
Russell Tsuchida, Cheng Soon Ong
Principal Component Analysis (PCA) and its exponential family extensions have three components: observations, latents and parameters of a linear transformation. We consider a gener…
Gaussian Process Bandits with Aggregated Feedback
Mengyan Zhang, Russell Tsuchida, Cheng Soon Ong
We consider the continuum-armed bandits problem, under a novel setting of recommending the best arms within a fixed budget under aggregated feedback. This is motivated by applicati…
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks
Russell Tsuchida, Tim Pearce, Chris van der Heide +2
Analysing and computing with Gaussian processes arising from infinitely wide neural networks has recently seen a resurgence in popularity. Despite this, many explicit covariance fu…