New method extracts aleatoric and epistemic uncertainties from regression-based neural networks.
problem Need for principled uncertainty reasoning in machine learning systems.
method Learning evidential distributions for aleatoric and epistemic uncertainties.
result Allows for the simultaneous extraction of both uncertainties without sampling or out-of-distribution data.
DER uses neural nets to better handle uncertainty in machine learning.
problem Need for principled uncertainty reasoning in safety-critical domains.
method Uncertainty-aware regression-based neural networks (NNs) with evidential distributions.
result DER shows promise over traditional methods but is a heuristic.
Proposes a deep model for Bayesian quantile regression without Gaussian assumptions.
problem Uncertainty quantification from single forward-pass models is computationally expensive and restrictive.
method Deep evidential learning for Bayesian quantile regression.
result Achieves calibrated uncertainties on non-Gaussian distributions.
Survey on Evidential Deep Learning for uncertainty estimation in deep neural networks.
problem Uncertainty estimation in deep neural networks with overhead or limited diversity.
method Evidential Deep Learning, parameterizing distributions over distributions.
result Single model and forward pass uncertainty estimation with a unified notation.
Enhances ENet's prediction accuracy while maintaining uncertainty estimation.
problem Gradient shrinkage problem in ENet's loss function.
method Proposes a multi-task learning framework with a modified MSE loss function.
result Improves ENet's prediction accuracy without losing uncertainty estimation.
Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust, and efficient measures of uncertainty are crucial. In this paper, we propose a novel method for training non-Bayesian NNs to estimate a continuous target as well as its associated evidence in order …
ECP method improves image classifier uncertainty sets.
problem Generating reliable uncertainty sets for deep classifiers.
method Evidential Conformal Prediction (ECP) based on EDL.
result ECP outperforms state-of-the-art methods in set size and adaptivity.
Proposes a simpler method for quantifying uncertainty in time-series with volatility clustering.
problem Uncertainty quantification for time-series with volatility clustering.
method Proposes a Scale Mixture Distribution to quantify return forecast uncertainty in neural networks.
result The proposed method provides a favorable complexity-accuracy trade-off and separates model parameters into subnetworks.
DAEDL improves EDL's OOD detection and classification performance by integrating feature space density.
problem Limited OOD detection and classification performance of EDL.
method Integrates feature space density with EDL's output and uses a novel parameterization.
result Demonstrates state-of-the-art performance across uncertainty estimation and classification tasks.
We propose a novel method for closed-form predictive distribution modeling with neural nets. In quantifying prediction uncertainty, we build on Evidential Deep Learning, which has been impactful as being both simple to implement and giving closed-form access to predictive uncertainty. We employ it to model aleatoric un…
Flexible evidential deep learning improves uncertainty quantification in machine learning.
problem Overconfident predictions in machine learning models can lead to serious consequences.
method Proposes flexible evidential deep learning (F-EDL) to model uncertainty over class probabilities using a flexible Dirichlet distribution.
result Empirically demonstrates state-of-the-art uncertainty quantification performance across diverse scenarios.
A new method improves uncertainty estimation in deep learning, especially for hard-to-label samples.
problem Improving uncertainty estimation for hard-to-label samples in deep learning.
method Introduces Fisher Information Matrix (FIM) to dynamically reweight objective loss terms.
result Consistently outperforms traditional evidential neural networks in uncertainty estimation tasks.
Evidential Softmax preserves multimodality in sparse probability distributions for generative models.
problem Sparse probability distributions in deep generative models make exact marginalization computationally intractable.
method Introduce ev-softmax, a sparse normalization function that preserves multimodality and can be trained with probabilistic loss functions.
result ev-softmax outperforms existing techniques in distributional accuracy and dimensionality reduction.
Study questions the reliability of uncertainty quantification in evidential deep learning.
problem Reliability of uncertainty quantification in evidential deep learning.
method Analysis of evidential deep learning methods, revealing their limitations and interpreting them as out-of-distribution detection algorithms.
result EDL methods are unreliable in quantifying uncertainty, even when effective on downstream tasks.
New method improves uncertainty calibration in deep learning.
problem Systematic overconfidence in EDL on out-of-distribution inputs.
method Density-Informed Pseudo-count EDL (DIP-EDL) separates class prediction from uncertainty.
result DIP-EDL achieves asymptotic concentration and enhances robustness and uncertainty calibration.
Deep Huber QRNs predict Huber quantiles for house prices.
problem Predicting more functionals of predictive probability distributions.
method Training a DL algorithm with the Huber quantile scoring function.
result DHQRNs provide satisfactory absolute performance in house price prediction.
ProbFM provides principled uncertainty quantification for financial forecasting.
problem Lack of principled uncertainty quantification in financial applications.
method Probabilistic Time Series Foundation Model with Uncertainty Decomposition using Deep Evidential Regression (DER).
result DER maintains competitive forecasting accuracy while providing explicit epistemic-aleatoric uncertainty decomposition.
Evidential clustering is an approach to clustering in which cluster-membership uncertainty is represented by a collection of Dempster-Shafer mass functions forming an evidential partition. In this paper, we propose to construct these mass functions by bootstrapping finite mixture models. In the first step, we compute b…
Simplified plug-in loss approximates EDL for reliable uncertainty estimation.
problem Efficient and reliable uncertainty estimation in real-world sensor-based learning systems.
method Approximate Dirichlet expected objectives with plug-in losses evaluated at the Dirichlet mean.
result Plug-in losses provide comparable predictive accuracy and selective prediction performance to classical EDL, while being simpler to implement.
EGMM improves clustering by better handling uncertainty with evidential framework.
problem Clustering uncertainty and complexity in data.
method Proposes EGMM, a new model-based clustering algorithm using belief functions and EM algorithm.
result EGMM generates more informative evidential partitions and outperforms other algorithms.
PG-EVIKAL refines molecular property predictions using neighbor fusion and evidential neural networks.
problem Improving molecular property predictions using test-time neighbor fusion.
method Adapting evidential neural networks to refine predictions by re-ranking structurally similar neighbors.
result PG-EVIKAL reduces RMSE on 14 out of 16 molecular datasets, improving calibration and sequential refinement.
NN-EVCLUS uses neural networks to cluster data with uncertainty.
problem Clustering data with uncertainty and handling outliers.
method NN-EVCLUS learns a neural network to map attributes to mass functions, minimizing discrepancy between dissimilarities and conflict.
result NN-EVCLUS outperforms existing methods in clustering tasks.
ConfEviSurrogate improves surrogate model accuracy and uncertainty quantification.
problem Uncertainty in surrogate models hinders reliable analysis.
method Introduces ConfEviSurrogate, a novel model that learns evidential distributions, separates uncertainty sources, and provides reliable prediction intervals.
result Demonstrates accurate predictions and robust uncertainty estimates in various simulations.
EDICT learns evidential distributions for irregular time series, improving predictions and uncertainty quantification.
problem Challenges in predicting and characterizing uncertainty for irregular time series data.
method EDICT (Evidential Distributions for Irregular Time Series) learns a continuous-time evidential distribution.
result EDICT achieves competitive performance on time series classification tasks and provides better uncertainty quantification.
This research improves neural network uncertainty estimates and reliability.
problem Lack of inherent uncertainty estimates and variability in softmax scores.
method Ensemble-based Dirichlet modeling with method of moments estimator.
result Improved stability and predictive uncertainty estimates.
FAML addresses biased evidence learning in multi-view learning, improving fairness and prediction reliability.
problem Biased evidence learning in multi-view learning, leading to unreliable uncertainty estimation.
method FAML introduces an adaptive prior and fairness constraint to balance evidence allocation across views.
result FAML enhances fairness and improves prediction reliability compared to state-of-the-art methods.
Paper introduces efficient uncertainty estimation in LLMs without multiple forward passes.
problem Accurate uncertainty quantification in LLMs remains challenging.
method Evidential Knowledge Distillation to create compact student models.
result Efficient uncertainty estimation achieved with single forward pass.
Novel method for Bayesian model comparison using deep learning.
problem Comparing complex models in science with intractable likelihood functions.
method Simulation-based, purely deep learning approach that amortizes model fitting costs.
result Achieves excellent results in accuracy, calibration, and efficiency.
Efficient AL method improves CNN performance with minimal labeled data.
problem Limited labeled data for training CNNs.
method Deep Evidential Active Learning (DEAL) using Dirichlet density.
result DEAL consistently outperforms other AL approaches and requires minimal computational resources.
We present a framework that enables estimation of low-dimensional sub-resolution reservoir properties directly from seismic data, without requiring the solution of a high dimensional seismic inverse problem. Our workflow is based on the Bayesian evidential learning approach and exploits learning the direct relation bet…
Develops methods to improve reliability of deep learning for autonomous driving.
problem Safety concerns in deploying autonomous driving systems.
method Introduces a new criterion (true class probability) for estimating model confidence and learns it from data.
result Proposed method provides better failure prediction than current uncertainty measures.
Study benchmarks uncertainty quantification in chest X-ray classification.
problem Reliable uncertainty quantification for medical AI models.
method Evaluation of 13 uncertainty quantification methods on MIMIC-CXR-JPG dataset.
result Insights into effectiveness and disentanglement of epistemic and aleatoric uncertainties.
Novel approach uses ENN for UQ in gust predictions, reducing RMSE and improving confidence.
problem Reducing bias and uncertainty in wind gust predictions.
method Evidential Neural Network (ENN) with Explainable AI.
result 47% reduction in RMSE, 95% coverage of observed gusts at 179 out of 266 stations.
Efficient approach improves prediction calibration for domain shifts.
problem Improving uncertainty-aware predictions for domain shifts.
method Combining entropy-encouraging and adversarial calibration losses.
result Substantially outperforms existing approaches in domain drift calibration.
Wasserstein gradient boosting predicts probability distributions for supervised learning.
problem Distribution-valued supervised learning where outputs are probability distributions.
method Fits a new weak learner to Wasserstein gradients of loss functionals of probability distributions.
result Superior performance in probabilistic prediction compared to existing methods.
Study evaluates uncertainty estimation methods in binary classification models.
problem Difficulty in quantifying uncertainty in complex models like deep learning.
method Approximate Bayesian inference with synthetic datasets and empirical tests.
result Deep learning-based algorithms do not consistently reflect lack of evidence for out-of-distribution data.
Deterministic neural nets have been shown to learn effective predictors on a wide range of machine learning problems. However, as the standard approach is to train the network to minimize a prediction loss, the resultant model remains ignorant to its prediction confidence. Orthogonally to Bayesian neural nets that indi…
The state-of-the-art solutions for Aspect-Level Sentiment Analysis (ALSA) were built on a variety of deep neural networks (DNN), whose efficacy depends on large amounts of accurately labeled training data. Unfortunately, high-quality labeled training data usually require expensive manual work, and may thus not be readi…
Deep neural nets can estimate regression with dependent data without the curse of dimensionality.
problem Regression with dependent data and structural assumptions on the regression function.
method Deep recurrent neural network estimate under suitable structural assumptions.
result Deep neural nets can circumvent the curse of dimensionality for regression with dependent data.
Deep Evidence Regression improves credit risk prediction uncertainty.
problem Quantifying uncertainty in credit risk predictions.
method Applying Deep Evidence Regression to credit risk settings.
result Demonstrated improved prediction of Loss Given Default.
This paper extends neural collapse to regression problems, revealing key features and structures.
problem Understanding the structure learned by deep neural networks in regression tasks.
method Established Neural Regression Collapse (NRC) across different models, analyzing feature and weight alignments.
result Deep neural regression models exhibit a collapsed feature space, aligning with target dimensions and covariances.
Proposes a method to estimate drug sensitivity uncertainty using deep regression forests.
problem Lack of confidence intervals in deep learning models for critical tasks.
method Uses Deep Regression Forests to estimate variance and uncertainty for drug sensitivity prediction.
result Improves efficiency and coverage of uncertainty estimates for drug sensitivity predictions.
Deep single-index Fréchet regression for metric space-valued outputs
problem Predicting outputs in non-Euclidean spaces
method DeSI (Deep Single-Index Fréchet Regression)
result Interpretable index direction for inputs
Deep-HGP uses Bayesian nonparametric approach for complex data regression.
problem Complex data regression with compositional structures.
method Deep Gaussian processes with a squared-exponential kernel, data-driven lengthscale parameters.
result Posterior distribution optimally recovers unknown true regression curve in terms of quadratic loss.
DRE combines DNN with random feature regression for efficient neural network design.
problem Designing and training deep neural networks (DNN) efficiently and effectively.
method DRE architecture with two-layer neural networks, randomly drawn input and output weights trained with linear ridge regression.
result DRE outperforms state-of-the-art DNN in many data sets with lower computational cost.
Flexible framework for deep distributional regression models.
problem Learning conditional distributions from semi-structured data.
method Combines additive regression models with deep networks using TensorFlow.
result State-of-the-art predictive performance with interpretability.
Deep model tackles claim size modeling with quantile-based regression.
problem Actuarial claim size modeling difficulty with no simple distribution.
method Deep composite regression model with quantile splicing point.
result Deep neural network regression models show superiority over classical approaches.
Deep neural networks estimate regression functions on manifolds.
problem Estimating regression functions on manifolds from data.
method Fully connected deep neural networks with ReLU activation, analyzing convergence rates.
result Estimates achieve a rate of convergence dependent on manifold dimension, not predictor dimension.