research
∙
02/22/2021
A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization
Machine learning practitioners invest significant manual and computation...
share
research
∙
12/14/2020
Variational State and Parameter Estimation
This paper considers the problem of computing Bayesian estimates of both...
research
∙
12/08/2020
Variational Nonlinear System Identification
This paper considers parameter estimation for nonlinear state-space mode...
research
∙
03/13/2020
The Elliptical Processes: a New Family of Flexible Stochastic Processes
We present the elliptical processes-a new family of stochastic processes...
research
∙
02/05/2020
Linearly Constrained Neural Networks
We present an approach to designing neural network based models that wil...
research
∙
09/03/2019
Stochastic quasi-Newton with line-search regularization
In this paper we present a novel quasi-Newton algorithm for use in stoch...
research
∙
01/28/2019
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding
We address the problem of inferring the causal effect of an exposure on ...
research
∙
09/29/2018
A fast quasi-Newton-type method for large-scale stochastic optimisation
During recent years there has been an increased interest in stochastic a...
research
∙
02/12/2018
