arXiv research

Flexible nonstationary Gaussian process with neural network parameters.

The analysis of nonstationary time series is of great importance in many scientific fields such as physics and neuroscience. In recent years, Gaussian process regression has attracted substantial attention as a robust and powerful method for analyzing time series. In this paper, we introduce a new framework for analyzi…

A new method for nonstationary Gaussian processes using Fourier features.

Proposes a new model for EHR data using time-dependent Gaussian processes.

ConvNets improve nonstationary covariance estimation for large-scale spatial data.

A new algorithm maximizes entropy or mutual information for efficient inference of nonstationary Gaussian processes.

Develops nonstationary MOGP kernels for better performance.

LISAL learns nonstationary spatio-temporal models efficiently.

End-to-end probabilistic inference improves audio signal processing.

Improves spatial statistics with nonstationary kernels.

We introduce a new class of nonstationary kernels, which we derive as covariance functions of a novel family of stochastic processes we refer to as string Gaussian processes (string GPs). We construct string GPs to allow for multiple types of local patterns in the data, while ensuring a mild global regularity condition…

A new method warps inputs to learn nonstationary kernels efficiently.

Online learning of nonstationary functions using Gaussian processes.

Bayesian optimization has shown to be a fundamental global optimization algorithm in many applications: ranging from automatic machine learning, robotics, reinforcement learning, experimental design, simulations, etc. The most popular and effective Bayesian optimization relies on a surrogate model in the form of a Gaus…

A new method optimizes in nonstationary environments with many arms efficiently.

Optimizes spectral density estimation for stationary and nonstationary processes.

New method tests independence with single nonstationary time series.

RLGP model improves robustness and accuracy for discontinuous response surfaces.

New method infers causal relationships from nonstationary time series data.

Gaussian processes (GPs) provide a nonparametric representation of functions. However, classical GP inference suffers from high computational cost and it is difficult to design nonstationary GP priors in practice. In this paper, we propose a sparse Gaussian process model, EigenGP, based on the Karhunen-Loeve (KL) expan…

New kernel enables exact GP analysis of massive datasets.

SA-REMBO adapts to nonstationary high-dimensional optimization.

Bayesian optimization has become a fundamental global optimization algorithm in many problems where sample efficiency is of paramount importance. Recently, there has been proposed a large number of new applications in fields such as robotics, machine learning, experimental design, simulation, etc. In this paper, we foc…

Bayesian Complementary Kernelized Learning models complex spatiotemporal data.

New algorithm speeds up optimization of Gaussian process models.

Model nonstationary spatial processes using normalizing flows.

CBGP boosts GP covariance to model spatiotemporal irregularities.

Deep models predict spatial phenomena with better accuracy.

This paper presents a novel approach for approximate integration over the uncertainty of noise and signal variances in Gaussian process (GP) regression. Our efficient and straightforward approach can also be applied to integration over input dependent noise variance (heteroscedasticity) and input dependent signal varia…

Gaussian processes retain the linear model either as a special case, or in the limit. We show how this relationship can be exploited when the data are at least partially linear. However from the perspective of the Bayesian posterior, the Gaussian processes which encode the linear model either have probability of nearly…

A novel nonstationary permanental process relaxes kernel constraints and captures complex data patterns.

Deep Gaussian processes on manifolds improve performance on complex data.

We formulate and analyze a graphical model selection method for inferring the conditional independence graph of a high-dimensional nonstationary Gaussian random process (time series) from a finite-length observation. The observed process samples are assumed uncorrelated over time and having a time-varying marginal dist…

We study a special case of the problem of statistical learning without the i.i.d. assumption. Specifically, we suppose a learning method is presented with a sequence of data points, and required to make a prediction (e.g., a classification) for each one, and can then observe the loss incurred by this prediction. We go …

New framework models complex spatial data with basis functions and graphical vectors.

Flexible spatial models improve predictive performance over nonstationary alternatives.

Paper develops bandit algorithms for nonstationary nonconvex optimization.

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…

Bayesian optimization improves with nonstationary covariance functions.

New kernel model for natural language processing improves expressibility and scalability.

The paper tackles causal discovery and forecasting in nonstationary environments using state-space models.

Extends inequalities to nonstationary Markov chains for better risk bounds.

Quantum reservoir computing needs coherence influx for effective information processing.

A new method for separating mixed signals in space and time.

Paper uses RL to estimate long-term treatment effects efficiently.

New RL algorithm tackles nonstationary MDPs with linear approximations and varying rewards.

We use diffusion models to sample from complex GP priors in climate data.

Adaptive estimation for nonstationary time series reduces computational cost.