341 citations · 860 across the 36 of their papers we have counts for
7 papers · 1 filter
Attacks on Online Learners: a Teacher-Student Analysis
Riccardo Giuseppe Margiotta, Sebastian Goldt, Guido Sanguinetti
Machine learning models are famously vulnerable to adversarial attacks: small ad-hoc perturbations of the data that can catastrophically alter the model predictions. While a large…
Matching models across abstraction levels with Gaussian Processes
Giulio Caravagna, Luca Bortolussi, Guido Sanguinetti
Biological systems are often modelled at different levels of abstraction depending on the particular aims/resources of a study. Such different models often provide qualitatively co…
Expectation propagation for continuous time stochastic processes
Botond Cseke, David Schnoerr, Manfred Opper +1
We consider the inverse problem of reconstructing the posterior measure over the trajec- tories of a diffusion process from discrete time observations and continuous time constrain…
Unbiased Bayesian Inference for Population Markov Jump Processes via Random Truncations
Anastasis Georgoulas, Jane Hillston, Guido Sanguinetti
We consider continuous time Markovian processes where populations of individual agents interact stochastically according to kinetic rules. Despite the increasing prominence of such…
A Bayesian approach for structure learning in oscillating regulatory networks
D Trejo, AJ Millar, G Sanguinetti
Oscillations lie at the core of many biological processes, from the cell cycle, to circadian oscillations and developmental processes. Time-keeping mechanisms are essential to enab…
Factored expectation propagation for input-output FHMM models in systems biology
Botond Cseke, Guido Sanguinetti
We consider the problem of joint modelling of metabolic signals and gene expression in systems biology applications. We propose an approach based on input-output factorial hidden M…
Sparse Approximate Inference for Spatio-Temporal Point Process Models
Botond Cseke, Andrew Zammit Mangion, Tom Heskes +1
Spatio-temporal point process models play a central role in the analysis of spatially distributed systems in several disciplines. Yet, scalable inference remains computa- tionally…