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Andreas Ruttor

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5 papers

1 author row

NeurIPS Conference 2014 Conference Paper

Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data

Florian Stimberg
Andreas Ruttor
Manfred Opper

We introduce a model where the rate of an inhomogeneous Poisson process is modified by a Chinese restaurant process. Applying a MCMC sampler to this model allows us to do posterior Bayesian inference about the number of states in Poisson-like data. Our sampler is shown to get accurate results for synthetic data and we apply it to V1 neuron spike data to find discrete firing rate states depending on the orientation of a stimulus.

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NeurIPS Conference 2013 Conference Paper

Approximate Gaussian process inference for the drift function in stochastic differential equations

Andreas Ruttor
Philipp Batz
Manfred Opper

We introduce a nonparametric approach for estimating drift functions in systems of stochastic differential equations from incomplete observations of the state vector. Using a Gaussian process prior over the drift as a function of the state vector, we develop an approximate EM algorithm to deal with the unobserved, latent dynamics between observations. The posterior over states is approximated by a piecewise linearized process and the MAP estimation of the drift is facilitated by a sparse Gaussian process regression.

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NeurIPS Conference 2011 Conference Paper

Inference in continuous-time change-point models

Florian Stimberg
Manfred Opper
Guido Sanguinetti
Andreas Ruttor

We consider the problem of Bayesian inference for continuous time multi-stable stochastic systems which can change both their diffusion and drift parameters at discrete times. We propose exact inference and sampling methodologies for two specific cases where the discontinuous dynamics is given by a Poisson process and a two-state Markovian switch. We test the methodology on simulated data, and apply it to two real data sets in finance and systems biology. Our experimental results show that the approach leads to valid inferences and non-trivial insights.

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NeurIPS Conference 2010 Conference Paper

Approximate inference in continuous time Gaussian-Jump processes

Manfred Opper
Andreas Ruttor
Guido Sanguinetti

We present a novel approach to inference in conditionally Gaussian continuous time stochastic processes, where the latent process is a Markovian jump process. We first consider the case of jump-diffusion processes, where the drift of a linear stochastic differential equation can jump at arbitrary time points. We derive partial differential equations for exact inference and present a very efficient mean field approximation. By introducing a novel lower bound on the free energy, we then generalise our approach to Gaussian processes with arbitrary covariance, such as the non-Markovian RBF covariance. We present results on both simulated and real data, showing that the approach is very accurate in capturing latent dynamics and can be useful in a number of real data modelling tasks.

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NeurIPS Conference 2004 Conference Paper

Synchronization of neural networks by mutual learning and its application to cryptography

Einat Klein
Rachel Mislovaty
Ido Kanter
Andreas Ruttor
Wolfgang Kinzel

Two neural networks that are trained on their mutual output synchronize to an identical time dependant weight vector. This novel phenomenon can be used for creation of a secure cryptographic secret-key using a public channel. Several models for this cryptographic system have been suggested, and have been tested for their security under different sophis- ticated attack strategies. The most promising models are networks that involve chaos synchronization. The synchronization process of mutual learning is described analytically using statistical physics methods.

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