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Found 9 papers, 1 papers with code
A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization
Machine learning practitioners invest significant manual and computational resources in finding suitable learning rates for optimization algorithms.
Variational State and Parameter Estimation
In this work, a variational approach is used to provide an assumed density which approximates the desired, intractable, distribution.
Variational System Identification for Nonlinear State-Space Models
This paper considers parameter estimation for nonlinear state-space models, which is an important but challenging problem.
The Elliptical Processes: a Family of Fat-tailed Stochastic Processes
We present the elliptical processes -- a family of non-parametric probabilistic models that subsumes the Gaussian process and the Student-t process.
Linearly Constrained Neural Networks
We present a novel approach to modelling and learning vector fields from physical systems using neural networks that explicitly satisfy known linear operator constraints.
Matrix Multilayer Perceptron
Models that output a vector of responses given some inputs, in the form of a conditional mean vector, are at the core of machine learning.
Stochastic quasi-Newton with line-search regularization
In this paper we present a novel quasi-Newton algorithm for use in stochastic optimisation.
Stochastic quasi-Newton with adaptive step lengths for large-scale problems
We provide a numerically robust and fast method capable of exploiting the local geometry when solving large-scale stochastic optimisation problems.
Ancestor Sampling for Particle Gibbs
We present a novel method in the family of particle MCMC methods that we refer to as particle Gibbs with ancestor sampling (PG-AS).
