Adaptive Sampling to Reduce Epistemic Uncertainty Using Prediction Interval-Generation Neural Networks

• URL: http://arxiv.org/abs/2412.10570v1
• Date: Fri, 13 Dec 2024 21:21:47 GMT
• Title: Adaptive Sampling to Reduce Epistemic Uncertainty Using Prediction Interval-Generation Neural Networks
• Authors: Giorgio Morales, John Sheppard,
• Abstract summary: This paper presents an adaptive sampling approach designed to reduce epistemic uncertainty in predictive models. Our primary contribution is the development of a metric that estimates potential epistemic uncertainty. A batch sampling strategy based on Gaussian processes (GPs) is also proposed. We test our approach on three unidimensional synthetic problems and a multi-dimensional dataset based on an agricultural field for selecting experimental fertilizer rates.
• Score: 0.0
• License:
• Abstract: Obtaining high certainty in predictive models is crucial for making informed and trustworthy decisions in many scientific and engineering domains. However, extensive experimentation required for model accuracy can be both costly and time-consuming. This paper presents an adaptive sampling approach designed to reduce epistemic uncertainty in predictive models. Our primary contribution is the development of a metric that estimates potential epistemic uncertainty leveraging prediction interval-generation neural networks. This estimation relies on the distance between the predicted upper and lower bounds and the observed data at the tested positions and their neighboring points. Our second contribution is the proposal of a batch sampling strategy based on Gaussian processes (GPs). A GP is used as a surrogate model of the networks trained at each iteration of the adaptive sampling process. Using this GP, we design an acquisition function that selects a combination of sampling locations to maximize the reduction of epistemic uncertainty across the domain. We test our approach on three unidimensional synthetic problems and a multi-dimensional dataset based on an agricultural field for selecting experimental fertilizer rates. The results demonstrate that our method consistently converges faster to minimum epistemic uncertainty levels compared to Normalizing Flows Ensembles, MC-Dropout, and simple GPs.

Related papers

• Neural Conformal Control for Time Series Forecasting [54.96087475179419]
  We introduce a neural network conformal prediction method for time series that enhances adaptivity in non-stationary environments. Our approach acts as a neural controller designed to achieve desired target coverage, leveraging auxiliary multi-view data with neural network encoders. We empirically demonstrate significant improvements in coverage and probabilistic accuracy, and find that our method is the only one that combines good calibration with consistency in prediction intervals.
  arXiv  Detail & Related papers  (2024-12-24T03:56:25Z)
• Estimating Epistemic and Aleatoric Uncertainty with a Single Model [5.871583927216653]
  We introduce a new approach to ensembling, hyper-diffusion models (HyperDM) HyperDM offers prediction accuracy on par with, and in some cases superior to, multi-model ensembles. We validate our method on two distinct real-world tasks: x-ray computed tomography reconstruction and weather temperature forecasting.
  arXiv  Detail & Related papers  (2024-02-05T19:39:52Z)
• Source-Free Unsupervised Domain Adaptation with Hypothesis Consolidation of Prediction Rationale [53.152460508207184]
  Source-Free Unsupervised Domain Adaptation (SFUDA) is a challenging task where a model needs to be adapted to a new domain without access to target domain labels or source domain data. This paper proposes a novel approach that considers multiple prediction hypotheses for each sample and investigates the rationale behind each hypothesis. To achieve the optimal performance, we propose a three-step adaptation process: model pre-adaptation, hypothesis consolidation, and semi-supervised learning.
  arXiv  Detail & Related papers  (2024-02-02T05:53:22Z)
• Neural parameter calibration and uncertainty quantification for epidemic forecasting [0.0]
  We apply a novel and powerful computational method to the problem of learning probability densities on contagion parameters. Using a neural network, we calibrate an ODE model to data of the spread of COVID-19 in Berlin in 2020. We show convergence of our method to the true posterior on a simplified SIR model of epidemics, and also demonstrate our method's learning capabilities on a reduced dataset.
  arXiv  Detail & Related papers  (2023-12-05T21:34:59Z)
• Variational Inference with Coverage Guarantees in Simulation-Based Inference [18.818573945984873]
  We propose Conformalized Amortized Neural Variational Inference (CANVI) CANVI constructs conformalized predictors based on each candidate, compares the predictors using a metric known as predictive efficiency, and returns the most efficient predictor. We prove lower bounds on the predictive efficiency of the regions produced by CANVI and explore how the quality of a posterior approximation relates to the predictive efficiency of prediction regions based on that approximation.
  arXiv  Detail & Related papers  (2023-05-23T17:24:04Z)
• MetaRF: Differentiable Random Forest for Reaction Yield Prediction with a Few Trails [58.47364143304643]
  In this paper, we focus on the reaction yield prediction problem. We first put forth MetaRF, an attention-based differentiable random forest model specially designed for the few-shot yield prediction. To improve the few-shot learning performance, we further introduce a dimension-reduction based sampling method.
  arXiv  Detail & Related papers  (2022-08-22T06:40:13Z)
• Near-optimal inference in adaptive linear regression [60.08422051718195]
  Even simple methods like least squares can exhibit non-normal behavior when data is collected in an adaptive manner. We propose a family of online debiasing estimators to correct these distributional anomalies in at least squares estimation. We demonstrate the usefulness of our theory via applications to multi-armed bandit, autoregressive time series estimation, and active learning with exploration.
  arXiv  Detail & Related papers  (2021-07-05T21:05:11Z)
• Rapid Risk Minimization with Bayesian Models Through Deep Learning Approximation [9.93116974480156]
  We introduce a novel combination of Bayesian Models (BMs) and Neural Networks (NNs) for making predictions with a minimum expected risk. Our approach combines the data efficiency and interpretability of a BM with the speed of a NN. We achieve risk minimized predictions significantly faster than standard methods with a negligible loss on the testing dataset.
  arXiv  Detail & Related papers  (2021-03-29T15:08:25Z)
• Sampling-free Variational Inference for Neural Networks with Multiplicative Activation Noise [51.080620762639434]
  We propose a more efficient parameterization of the posterior approximation for sampling-free variational inference. Our approach yields competitive results for standard regression problems and scales well to large-scale image classification tasks.
  arXiv  Detail & Related papers  (2021-03-15T16:16:18Z)
• Towards Trustworthy Predictions from Deep Neural Networks with Fast Adversarial Calibration [2.8935588665357077]
  We propose an efficient yet general modelling approach for obtaining well-calibrated, trustworthy probabilities for samples obtained after a domain shift. We introduce a new training strategy combining an entropy-encouraging loss term with an adversarial calibration loss term and demonstrate that this results in well-calibrated and technically trustworthy predictions.
  arXiv  Detail & Related papers  (2020-12-20T13:39:29Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.