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Found 15 papers, 4 papers with code
Divide, Conquer, Combine Bayesian Decision Tree Sampling
A challenge for existing MCMC approaches is proposing joint changes in both the tree structure and the decision parameters that result in efficient sampling.
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.
Gaussian Variational State Estimation for Nonlinear State-Space Models
In this paper, the problem of state estimation, in the context of both filtering and smoothing, for nonlinear state-space models is considered.
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.
Learning Continuous Occupancy Maps with the Ising Process Model
The method is quite attractive as it requires only a small number of hyperparameters to be trained, and is computationally efficient.
Deep kernel learning for integral measurements
Deep kernel learning refers to a Gaussian process that incorporates neural networks to improve the modelling of complex functions.
Stochastic quasi-Newton with line-search regularization
In this paper we present a novel quasi-Newton algorithm for use in stochastic optimisation.
A fast quasi-Newton-type method for large-scale stochastic optimisation
During recent years there has been an increased interest in stochastic adaptations of limited memory quasi-Newton methods, which compared to pure gradient-based routines can improve the convergence by incorporating second order information.
Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals
Pseudo-marginal Metropolis-Hastings (pmMH) is a versatile algorithm for sampling from target distributions which are not easy to evaluate point-wise.
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.
Constructing Metropolis-Hastings proposals using damped BFGS updates
The computation of Bayesian estimates of system parameters and functions of them on the basis of observed system performance data is a common problem within system identification.
Computation Computational Finance
Linearly constrained Gaussian processes
We consider a modification of the covariance function in Gaussian processes to correctly account for known linear constraints.
Newton-based maximum likelihood estimation in nonlinear state space models
Maximum likelihood (ML) estimation using Newton's method in nonlinear state space models (SSMs) is a challenging problem due to the analytical intractability of the log-likelihood and its gradient and Hessian.
