arXiv research

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

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

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 tuning of latent space for non-stationary data.

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 …

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

This work develops discrete Gaussian models for vector-valued data on triangular meshes.

New algorithms reduce regret in non-stationary bandits with increasing payoffs.

Generative models improve inverse problems by providing tailored priors.

New definition resolves ambiguity in non-stationary bandit classification.

SmoothFBO tackles non-stationary functional bilevel optimization.

Proposes a new algorithm for non-stationary bandits.

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

Study incentivizes exploration in non-stationary MAB with compensation.

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

Proposes a method to forecast non-stationary time series.

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 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-…

Algorithm adapts to non-stationary rewards without prior knowledge.

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…

Unified approach for non-stationary and clustered bandits.

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…

Non-stationarity appears in many online applications such as web search and advertising. In this paper, we study the online learning to rank problem in a non-stationary environment where user preferences change abruptly at an unknown moment in time. We consider the problem of identifying the K most attractive items and…

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

Paper tackles non-stationary kernelized bandits with near-optimal algorithm.

The paper provides exact multivariate amplitude distributions for non-stationary Gaussian or algebraic fluctuations.

Flexible GP model improves wind power prediction accuracy.

Develops large-sample theory for non-stationary source separation.

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

Financial markets are complex environments that produce enormous amounts of noisy and non-stationary data. One fundamental problem is online portfolio selection, the goal of which is to exploit this data to sequentially select portfolios of assets to achieve positive investment outcomes while managing risks. Various al…

Extends geometric approach to model non-stationary extremal dependence.

Predictive sampling improves on Thompson sampling for non-stationary bandit environments.

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

Algorithm minimizes regret in non-stationary dueling bandits with unknown parameters.

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 …

MetaCURL tackles non-stationary MDPs with optimal dynamic regret.

New algorithm tackles non-stationary delayed feedback in recommender systems.

New algorithm for non-stationary bandits with slow drifts.

CNNs predict spatial fields from sparse data.

In this short report, we investigate the ability of the DCCA coefficient to measure correlation level between non-stationary series. Based on a wide Monte Carlo simulation study, we show that the DCCA coefficient can estimate the correlation coefficient accurately regardless the strength of non-stationarity (measured b…

This paper proposes non-stationary factor models for financial stress in the UK.

Framework infers Langevin dynamics from stochastic observations of latent systems.

A defining feature of non-stationary systems is the time dependence of their statistical parameters. Measured time series may exhibit Gaussian statistics on short time horizons, due to the central limit theorem. The sample statistics for long time horizons, however, averages over the time-dependent parameters. To model…

KeRNS tackles non-stationary reinforcement learning in metric spaces.

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…

Study shows optimal model performance at critical level of feature learning.

PROPO tackles non-stationary MDPs with efficient policy optimization.