arXiv research

HeMPPCAT improves PCA for data with varying noise.

Novel criterion identifies heteroscedastic noise in causal discovery.

ALPCAHUS clusters data from multiple subspaces with varying noise.

We propose a robust method to estimate heteroscedastic noise models using Student's t-distribution.

Bayesian optimisation is a sample-efficient search methodology that holds great promise for accelerating drug and materials discovery programs. A frequently-overlooked modelling consideration in Bayesian optimisation strategies however, is the representation of heteroscedastic aleatoric uncertainty. In many practical a…

Principal Component Analysis (PCA) is a method for estimating a subspace given noisy samples. It is useful in a variety of problems ranging from dimensionality reduction to anomaly detection and the visualization of high dimensional data. PCA performs well in the presence of moderate noise and even with missing data, b…

We solve matrix denoising with both row and column correlations, setting limits and designing optimal methods.

Proposes a method to improve classification robustness against label noise.

In the stochastic bandit problem, the goal is to maximize an unknown function via a sequence of noisy evaluations. Typically, the observation noise is assumed to be independent of the evaluation point and to satisfy a tail bound uniformly on the domain; a restrictive assumption for many applications. In this work, we c…

Study on financial systems using perturbed unimodal maps with heteroscedastic noise.

Study on online regression with noise, achieving near-optimal regret bounds.

Bayesian consensus improves accuracy of forecasts from miscalibrated sources.

Simple method improves deep classifier accuracy under noisy labels.

Presents SPEED, an algorithm for optimal policy evaluation in linear bandits with heteroscedastic noise.

ALPCAH improves PCA for noisy data samples.

ALPCAH improves PCA for noisy data by estimating sample-wise noise variances.

Bayesian optimisation tackles stochastic MPC hyper-parameter tuning.

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…

CLAPS improves conformal regression by adaptively scaling interval widths based on last-layer Laplace uncertainty.

Detects model misspecifications in causal models using observational data.

A new noise model for preferential Bayesian optimization using user anchors.

New model identifies patient-specific disease root causes.

The performance of the Lasso is well understood under the assumptions of the standard linear model with homoscedastic noise. However, in several applications, the standard model does not describe the important features of the data. This paper examines how the Lasso performs on a non-standard model that is motivated by …

Paper proposes GPM for heteroscedastic PCA estimation.

Heteroscedastic regression considering the varying noises among observations has many applications in the fields like machine learning and statistics. Here we focus on the heteroscedastic Gaussian process (HGP) regression which integrates the latent function and the noise function together in a unified non-parametric B…

Study online pricing with contextual elasticity and heteroscedastic valuation.

Improved heteroscedastic regression using neural networks with provably accurate mean estimates and calibrated variance.

Develops statistical confidence sets for multidimensional scaling.

Bayesian neural network models improve uncertainty quantification in multivariate regression.

New method learns DAGs from noisy data without identifiability assumptions.

We prove a new and general concentration inequality for the excess risk in least-squares regression with random design and heteroscedastic noise. No specific structure is required on the model, except the existence of a suitable function that controls the local suprema of the empirical process. So far, only the case of…

To restore the historical sea surface temperatures (SSTs) better, it is important to construct a good calibration model for the associated proxies. In this paper, we introduce a new model for alkenone (${\rm{U}}_{37}^{\rm{K}'}$) based on the heteroscedastic Gaussian process (GP) regression method. Our nonparametric app…

Study uncovers statistical optimality of nonconvex tensor completion methods.

A simple method treats heteroscedastic variance variatively, improving model calibration and sample quality.

Flexible GP model improves wind power prediction accuracy.

Paper introduces $ρ$-Perfect to estimate model-human correlation in subjective datasets.

Improves deep learning performance on noisy datasets using inverse-variance weighting.

Efficient exploration remains a major challenge for reinforcement learning. One reason is that the variability of the returns often depends on the current state and action, and is therefore heteroscedastic. Classical exploration strategies such as upper confidence bound algorithms and Thompson sampling fail to appropri…

Improved image classification accuracy with a probabilistic model of label noise.

Improved heteroscedastic regression with machine learning techniques.

Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems. Nowadays, with the widespread deployment of GPS-enabled devices, it has become possible to crowdsource the collection of speed information to road users (e.g. through mobile applications or dedicated in-vehicle dev…

Proposes MvTPMSVM to improve multiview learning with reduced computational complexity.

ConvMMD improves inference in noisy data.

Bayesian optimisation (BO) is widely used to optimise stochastic black box functions. While most BO approaches focus on optimising conditional expectations, many applications require risk-averse strategies and alternative criteria accounting for the distribution tails need to be considered. In this paper, we propose ne…

This paper aims to address two fundamental challenges arising in eigenvector estimation and inference for a low-rank matrix from noisy observations: (1) how to estimate an unknown eigenvector when the eigen-gap (i.e. the spacing between the associated eigenvalue and the rest of the spectrum) is particularly small; (2) …

LCMQR improves prediction intervals by adapting to local heteroscedasticity.

Bayesian neural networks with latent variables are scalable and flexible probabilistic models: They account for uncertainty in the estimation of the network weights and, by making use of latent variables, can capture complex noise patterns in the data. We show how to extract and decompose uncertainty into epistemic and…

Generalized autoregressive conditional heteroscedasticity (GARCH) models have long been considered as one of the most successful families of approaches for volatility modeling in financial return series. In this paper, we propose an alternative approach based on methodologies widely used in the field of statistical mac…