arXiv research

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

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

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-…

Extends geometric approach to model non-stationary extremal dependence.

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 …

Flexible GP model improves wind power prediction accuracy.

Algorithm adapts to non-stationary rewards without prior knowledge.

Framework selects optimal historical data windows for non-stationary learning.

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

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

Framework infers Langevin dynamics from stochastic observations of latent systems.

Kernel-based tests detect dependencies in multivariate time series, including stationary and non-stationary data.

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…

New definition resolves ambiguity in non-stationary bandit classification.

A new optimizer d-AmsGrad improves deep learning for robot learning in non-stationary problems.

SmoothFBO tackles non-stationary functional bilevel optimization.

In this paper, we provide non-parametric statistical tools to test stationarity of microstructure noise in general hidden Ito semimartingales, and discuss how to measure liquidity risk using high frequency financial data. In particular, we investigate the impact of non-stationary microstructure noise on some volatility…

Adaptive tuning of latent space for non-stationary data.

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

New Hermite series estimator for Spearman rank correlation in non-stationary data.

Incremental learning from non-stationary data poses special challenges to the field of machine learning. Although new algorithms have been developed for this, assessment of results and comparison of behaviors are still open problems, mainly because evaluation metrics, adapted from more traditional tasks, can be ineffec…

Adaptive sparse GP model for non-stationary data.

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

The economic crisis in Argentina around year 2002 provides a unique opportunity for Econophysics studies. The available data on individual income are analyzed to show that they correspond to non stationary states. However, the rather restricted size of the data survey imposes difficulties that must be overcome through …

New algorithm reduces online learning regret by exploiting historical invariances.

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

We model non-stationary volume-price distributions with a log-normal distribution and collect the time series of its two parameters. The time series of the two parameters are shown to be stationary and Markov-like and consequently can be modelled with Langevin equations, which are derived directly from their series of …

New method optimizes policies in non-stationary environments.

Transformers achieve near-optimal dynamic regret in non-stationary reinforcement learning.

Study on consistency of ML methods for moving objects in non-stationary environments.

The concept of SCN offers a fast framework with universal approximation guarantee for lifelong learning of non-stationary data streams. Its adaptive scope selection property enables for proper random generation of hidden unit parameters advancing conventional randomized approaches constrained with a fixed scope of rand…

Study incentivizes exploration in non-stationary MAB with compensation.

New algorithm tackles non-stationary reinforcement learning with general function approximation.

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

Proposes a method to forecast non-stationary time series.

Bayesian method improves SOM training for dynamic data.

New definitions of ESP for quantum reservoir computing handle non-stationary systems.

Study combines VICReg and TNC for better encoding of non-stationary seismic signals.

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 …

OML-AD detects anomalies in non-stationary time series 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…

Image-to-image networks speed up SAR model parameter estimation.

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

Framework models supervised learning in non-stationary data.

Optimistic algorithm reduces regret in non-stationary linear MDPs.

BFNs use Bayesian inference and neural networks for generative modeling.

Non-linear shrinkage isn't optimal for portfolio optimization, especially when asset dependence is non-stationary.

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…