4 citations · 9 across the 4 of their papers we have counts for
3 papers · 1 filter
Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching
Cagatay Yildiz, Markus Heinonen, Jukka Intosalmi +2
We introduce a novel paradigm for learning non-parametric drift and diffusion functions for stochastic differential equation (SDE). The proposed model learns to simulate path distr…
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization
Umut Şimşekli, Çağatay Yıldız, Thanh Huy Nguyen +2
Recent studies have illustrated that stochastic gradient Markov Chain Monte Carlo techniques have a strong potential in non-convex optimization, where local and global convergence…
Learning unknown ODE models with Gaussian processes
Markus Heinonen, Cagatay Yildiz, Henrik Mannerström +2
In conventional ODE modelling coefficients of an equation driving the system state forward in time are estimated. However, for many complex systems it is practically impossible to…