arXiv research

In this paper, we obtain the finite-horizon and infinite-horizon ruin probability asymptotics for risk processes with claims of subexponential tails for non-stationary arrival processes that satisfy a large deviation principle. As a result, the arrival process can be dependent, non-stationary and non-renewal. We give t…

Develops a deep non-stationary kernel for non-stationary spatio-temporal point processes.

Unified review of methods for inferring non-stationary process parameters.

We propose non-stationary spectral kernels for Gaussian process regression. We propose to model the spectral density of a non-stationary kernel function as a mixture of input-dependent Gaussian process frequency density surfaces. We solve the generalised Fourier transform with such a model, and present a family of non-…

We characterize the sample size required for accurate graphical model selection from non-stationary samples. The observed data is modeled as a vector-valued zero-mean Gaussian random process whose samples are uncorrelated but have different covariance matrices. This model contains as special cases the standard setting …

ETGPSSM efficiently models high-dimensional, non-stationary systems with reduced complexity.

Model separates overall uncertainty into aleatoric and epistemic components for active learning.

Flexible GP model improves wind power prediction accuracy.

The paper extends NSGPs with $L^1$-regularization for sparsity and solves the resulting R-NSGP regression problem.

Paper introduces a neural network-based non-stationary influence kernel for complex event data.

The application of Gaussian processes (GPs) to large data sets is limited due to heavy memory and computational requirements. A variety of methods has been proposed to enable scalability, one of which is to exploit structure in the kernel matrix. Previous methods, however, cannot easily deal with non-stationary process…

This work explores variably scaled kernels to improve non-stationary Gaussian processes.

This paper develops the first method for the exact simulation of reflected Brownian motion (RBM) with non-stationary drift and infinitesimal variance. The running time of generating exact samples of non-stationary RBM at any time $t$ is uniformly bounded by $\mathcal{O}(1/\barγ^2)$ where $\barγ$ is the average drift of…

Enhances DGPs with adaptive RKHS Fourier features for better non-stationary pattern modeling.

We present a novel approach for fully non-stationary Gaussian process regression (GPR), where all three key parameters -- noise variance, signal variance and lengthscale -- can be simultaneously input-dependent. We develop gradient-based inference methods to learn the unknown function and the non-stationary model param…

Proposes a new Bayesian mixture of student-t processes for modeling non-stationary data.

This work improves Gaussian process regression for large, non-stationary data.

We introduce Latent Gaussian Process Regression which is a latent variable extension allowing modelling of non-stationary multi-modal processes using GPs. The approach is built on extending the input space of a regression problem with a latent variable that is used to modulate the covariance function over the training …

Inter-domain Deep Gaussian Processes improve inference for non-stationary data.

Framework infers Langevin dynamics from stochastic observations of latent systems.

Adaptive sparse GP model for non-stationary data.

We consider a classical risk process with arrival of claims following a non-stationary Hawkes process. We study the asymptotic regime when the premium rate and the baseline intensity of the claims arrival process are large, and claim size is small. The main goal of the article is to establish a diffusion approximation …

Designing a covariance function that represents the underlying correlation is a crucial step in modeling complex natural systems, such as climate models. Geospatial datasets at a global scale usually suffer from non-stationarity and non-uniformly smooth spatial boundaries. A Gaussian process regression using a non-stat…

A survey is performed of various Multi-Armed Bandit (MAB) strategies in order to examine their performance in circumstances exhibiting non-stationary stochastic reward functions in conjunction with delayed feedback. We run several MAB simulations to simulate an online eCommerce platform for grocery pick up, optimizing …

NVMDP framework tackles non-stationary MDPs with varying discount rates.

New method learns time-varying home field advantage in football.

Researchers use Gaussian processes with non-stationary kernels to model precipitation patterns in the Upper Indus Basin.

Optimistic algorithm reduces regret in non-stationary linear MDPs.

Study on fake stationary Volterra Heston model for non-stationary processes.

Pairs trading strategy improved using Ornstein-Uhlenbeck process.

ETGP improves multi-class classification efficiency.

A new kernel improves Gaussian process performance for non-stationary data.

Gaussian processes (GPs) are commonplace in spatial statistics. Although many non-stationary models have been developed, there is arguably a lack of flexibility compared to equipping each location with its own parameters. However, the latter suffers from intractable computation and can lead to overfitting. Taking the i…

KeRNS tackles non-stationary reinforcement learning in metric spaces.

Standard kernels such as Matérn or RBF kernels only encode simple monotonic dependencies within the input space. Spectral mixture kernels have been proposed as general-purpose, flexible kernels for learning and discovering more complicated patterns in the data. Spectral mixture kernels have recently been generalized in…

NAST generalizes scattering transform for non-stationary time series analysis.

The state space (SS) representation of Gaussian processes (GP) has recently gained a lot of interest. The main reason is that it allows to compute GPs based inferences in O(n), where $n$ is the number of observations. This implementation makes GPs suitable for Big Data. For this reason, it is important to provide a SS …

MetaCURL tackles non-stationary MDPs with optimal dynamic regret.

We consider the problem of learning over non-stationary ranking streams. The rankings can be interpreted as the preferences of a population and the non-stationarity means that the distribution of preferences changes over time. Our goal is to learn, in an online manner, the current distribution of rankings. The bottlene…

Framework models supervised learning in non-stationary data.

A neural RNN model adapts time steps for non-stationary time series data.

New RL algorithm tackles non-stationary environments with flexible policy updates.

A new learning strategy using two GP layers for inhomogeneous data.

New RL method tackles dynamic MDPs with evolving rewards and states.

The expressive power of Gaussian processes depends heavily on the choice of kernel. In this work we propose the novel harmonizable mixture kernel (HMK), a family of expressive, interpretable, non-stationary kernels derived from mixture models on the generalized spectral representation. As a theoretically sound treatmen…

A new fuzzy time series method for non-stationary data.

This paper proposes a novel Gaussian process approach to fault removal in time-series data. Fault removal does not delete the faulty signal data but, instead, massages the fault from the data. We assume that only one fault occurs at any one time and model the signal by two separate non-parametric Gaussian process model…

This work introduces a new model for complex stochastic processes.