Active Heteroscedastic Regression
An active learner is given a model class $\Theta$, a large sample of unlabeled data drawn from an underlying distribution and access to a labeling oracle that can provide a label for any of the unlabeled instances. The goal of the learner is to find a model $\theta \in \Theta$ that fits the data to a given accuracy while making as few label queries to the oracle as possible. In this work, we consider a theoretical analysis of the label requirement of active learning for regression under a heteroscedastic noise model, where the noise depends on the instance. We provide bounds on the convergence rates of active and passive learning for heteroscedastic regression. Our results illustrate that just like in binary classification, some partial knowledge of the nature of the noise can lead to significant gains in the label requirement of active learning.
Code (0)
등록된 구현이 없습니다.
Tasks
Active LearningBinary ClassificationregressionSimilar Papers 제목 키워드 기반
CAAL: Confidence-Aware Active Learning for Heteroscedastic Atmospheric Regression
Quantifying the impacts of air pollution on health and climate relies on key atmospheric particle properties such as toxicity and hygroscopicity. However, these properties typically require complex observational techniqu…
Active LearningEffective Bayesian Heteroscedastic Regression with Deep Neural Networks
Flexibly quantifying both irreducible aleatoric and model-dependent epistemic uncertainties plays an important role for complex regression problems. While deep neural networks in principle can provide this flexibility an…
Variational Variance: Simple, Reliable, Calibrated Heteroscedastic Noise Variance Parameterization
Brittle optimization has been observed to adversely impact model likelihoods for regression and VAEs when simultaneously fitting neural network mappings from a (random) variable onto the mean and variance of a dependent …
regressionTowards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression
Deep heteroscedastic regression models the mean and covariance of the target distribution through neural networks. The challenge arises from heteroscedasticity, which implies that the covariance is sample dependent and i…
Pseudo LabelregressionThe Effect of Heteroscedasticity on Regression Trees
Regression trees are becoming increasingly popular as omnibus predicting tools and as the basis of numerous modern statistical learning ensembles. Part of their popularity is their ability to create a regression predicti…
regression