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publications
Learning dynamical systems from data: a simple cross-validation perspective, part II: nonparametric kernel flows
Preprint, 2021
We condsider how to infer dynamical systems using non-parametric Kernel Flows.
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One-Shot Learning of Stochastic Differential Equations with Computational Graph Completion
Preprint, 2022
We consider the problem of learning Stochastic Differential Equations of the form dXt = f (Xt)dt + σ(Xt)dWt from one sample trajectory using Gaussian Processes.
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talks
Kernel Flows Demystified: Application to Regression
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Watch.
I present the content of my master’s thesis “Kernels Flows demystified: applications to regression”.
One shot learning of stochastic differential equations with Gaussian processes and computational graph completion.
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Download slides. I present an approach to recover the drift and volatility functions of a stochastic differential equation given data from one sample trajectory.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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tutorials
Baysian Optimization for Hyper-parameter tuning
Published:
A tutorial on how to use bayesian optimization with Gaussian Process surrogate modeling for tuning hyper-paramters.