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06/21/2023
Quantifying lottery tickets under label noise: accuracy, calibration, and complexity
Pruning deep neural networks is a widely used strategy to alleviate the ...
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05/18/2023
Attacks on Online Learners: a Teacher-Student Analysis
Machine learning models are famously vulnerable to adversarial attacks: ...
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07/13/2022
On the Robustness of Bayesian Neural Networks to Adversarial Attacks
Vulnerability to adversarial attacks is one of the principal hurdles to ...
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02/22/2021
Resilience of Bayesian Layer-Wise Explanations under Adversarial Attacks
We consider the problem of the stability of saliency-based explanations ...
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02/18/2021
Random Projections for Improved Adversarial Robustness
We propose two training techniques for improving the robustness of Neura...
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12/01/2020
Systematic errors in estimates of R_t from symptomatic cases in the presence of observation bias
We consider the problem of estimating the reproduction number R_t of an ...
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02/11/2020
Robustness of Bayesian Neural Networks to Gradient-Based Attacks
Vulnerability to adversarial attacks is one of the principal hurdles to ...
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01/31/2019
Geometric fluid approximation for general continuous-time Markov chains
Fluid approximations have seen great success in approximating the macro-...
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11/08/2018
Intrinsic Geometric Vulnerability of High-Dimensional Artificial Intelligence
The success of modern Artificial Intelligence (AI) technologies depends ...
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11/06/2017
Probabilistic Model Checking for Continuous Time Markov Chains via Sequential Bayesian Inference
Probabilistic model checking for systems with large or unbounded state s...
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06/07/2017
On learning the structure of Bayesian Networks and submodular function maximization
Learning the structure of dependencies among multiple random variables i...
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06/01/2017
Efficient Low-Order Approximation of First-Passage Time Distributions
We consider the problem of computing first-passage time distributions fo...
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08/23/2016
Approximation and inference methods for stochastic biochemical kinetics - a tutorial review
Stochastic fluctuations of molecule numbers are ubiquitous in biological...
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06/03/2016
Property-driven State-Space Coarsening for Continuous Time Markov Chains
Dynamical systems with large state-spaces are often expensive to thoroug...
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05/07/2016
Matching models across abstraction levels with Gaussian Processes
Biological systems are often modelled at different levels of abstraction...
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01/08/2016
Cox process representation and inference for stochastic reaction-diffusion processes
Complex behaviour in many systems arises from the stochastic interaction...
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12/18/2015
Expectation propagation for continuous time stochastic processes
We consider the inverse problem of reconstructing the posterior measure ...
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09/28/2015
Unbiased Bayesian Inference for Population Markov Jump Processes via Random Truncations
We consider continuous time Markovian processes where populations of ind...
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12/29/2013
Learning Temporal Logical Properties Discriminating ECG models of Cardiac Arrhytmias
We present a novel approach to learn the formulae characterising the eme...
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09/03/2013
On the Robustness of Temporal Properties for Stochastic Models
Stochastic models such as Continuous-Time Markov Chains (CTMC) and Stoch...
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05/17/2013
Factored expectation propagation for input-output FHMM models in systems biology
We consider the problem of joint modelling of metabolic signals and gene...
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05/17/2013