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06/09/2023
Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks
We present a new algorithmic framework, Intensity Profile Projection, fo...
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05/24/2023
Hierarchical clustering with dot products recovers hidden tree structure
In this paper we offer a new perspective on the well established agglome...
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10/27/2022
Implications of sparsity and high triangle density for graph representation learning
Recent work has shown that sparse graphs containing many triangles canno...
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08/24/2022
Discovering latent topology and geometry in data: a law of large dimension
Complex topological and geometric patterns often appear embedded in high...
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05/26/2022
Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods
Addressing the challenge of scaling-up epidemiological inference to comp...
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06/02/2021
Matrix factorisation and the interpretation of geodesic distance
Given a graph or similarity matrix, we consider the problem of recoverin...
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07/23/2020
An invitation to sequential Monte Carlo samplers
Sequential Monte Carlo samplers provide consistent approximations of seq...
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06/24/2020
Inference in Stochastic Epidemic Models via Multinomial Approximations
We introduce a new method for inference in stochastic epidemic models wh...
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04/15/2020
Dynamic Bayesian Neural Networks
We define an evolving in time Bayesian neural network called a Hidden Ma...
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06/25/2019
Dynamic time series clustering via volatility change-points
This note outlines a method for clustering time series based on a statis...
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02/05/2019
Exploiting locality in high-dimensional factorial hidden Markov models
We propose algorithms for approximate filtering and smoothing in high-di...
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12/04/2018
Parallelising Particle Filters with Butterfly Interactions
Bootstrap particle filter (BPF) is the corner stone of many popular algo...
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10/08/2018
The Viterbi process, decay-convexity and parallelized maximum a-posteriori estimation
The Viterbi process is the limiting maximum a-posteriori estimate of the...
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07/24/2018
Global consensus Monte Carlo
For Bayesian inference with large data sets, it is often convenient or n...
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11/26/2012