Paper recovers uncertainty from dynamic valuation rules.
problem Recovering latent uncertainty from observable valuation rules.
method Developed procedures to identify and characterize uncertainty structures from valuation rules.
result Valuation rules contain sufficient information to identify and recover uncertainty structures.
TRUST improves structure learning with tractable uncertainty.
problem Capturing uncertainty in structure learning for causal DAGs.
method Probabilistic circuits for posterior inference.
result Probabilistic circuits enhance structure learning quality and uncertainty.
This research tackles uncertainty estimation in autoregressive structured prediction tasks.
problem Ensuring safety and robustness of AI systems through accurate uncertainty estimation.
method Develops a unified probabilistic ensemble-based framework for token-level and sequence-level uncertainty estimation.
result Provides baselines for error and out-of-domain detection on translation and speech recognition datasets.
Model predicts composite structures assembly quality with input uncertainty.
problem Accurate prediction of dimensional deviations and residual stress in composite structures assembly.
method Neural Network Gaussian Process considering input uncertainty.
result NNGPIU model outperforms other methods for nonsmooth, nonlinear responses.
Introduces hierarchical uncertainty using U-sequences.
problem Tackles Ellsberg's paradox in multi-layer uncertainty.
method Uses category theory to construct U-sequences and endofunctors.
result Constructs a universal uncertainty space for multi-layer uncertainty.
Spatially-aware metrics improve uncertainty evaluation in segmentation.
problem Uncertainty evaluation metrics treat voxels independently, ignoring spatial context.
method Proposed three spatially aware metrics incorporating structural and boundary information.
result Improved alignment with clinically important factors and better discrimination between uncertainty patterns.
New framework improves reliability of learned representations by modeling uncertainty and structural constraints.
problem Uncertainty in learned representations treated as deterministic, leading to unreliable models.
method Proposes a principled framework for reliable representation learning with uncertainty-aware regularization and structural constraints.
result Improves stability, calibration, and robustness of learned representations.
Framework for pricing waterfall structures using simulation and uncertainty modeling.
problem Pricing complex structured finance instruments under uncertainty.
method Simulation-based uncertainty modeling, calibrated probability distributions, PyTorch implementation, Adjoint Algorithmic Differentiation (AAD).
result Efficient gradient computation for risk sensitivity analysis and optimization.
USNRT uses tree-structured learning to improve uncertainty quantification of variance networks.
problem Improving uncertainty quantification of variance networks.
method Tree-structured local neural network model that partitions feature space into regions for training region-specific neural networks to predict mean and variance.
result USNRT shows superior performance in estimating uncertainty with variances on UCI datasets compared to recent methods.
Enhances ocean floor mapping with adaptive uncertainty estimates.
problem Inaccurate bathymetric data for precise ocean modeling.
method Block-based conformal prediction with VQ-VAE architecture.
result Significant improvements in reconstruction quality and uncertainty estimation reliability.
Metrics assess uncertainty structure and distribution for regression models.
problem Quantifying uncertainty in high-dimensional and nonlinear regression tasks.
method Two bounded comparison metrics for uncertainty structure and distribution.
result DNNs and DNOs provide encouraging uncertainty metric values in high dimensions.
New metrics improve uncertainty estimation on graph data.
problem Current GNNs focus only on nodewise scores, limiting uncertainty estimation.
method Proposed edgewise metrics for uncertainty estimation on graphs.
result GNN models with structured prediction perform better in uncertainty estimation.
GEBM improves uncertainty quantification in graph neural networks.
problem Challenges in quantifying epistemic uncertainty in graph neural networks.
method Energy-based model (EBM) that aggregates uncertainty at different structural levels.
result Significantly improves predictive robustness and achieves best separation of in-distribution and out-of-distribution data.
SON learns SPDE solutions and uncertainty from noisy data.
problem Uncertainty quantification in SPDEs with unknown model uncertainties.
method Combining DeepONet and SNNs, SON models stochasticity and predicts uncertainty.
result SON accurately captures solution structure and quantifies predictive uncertainty.
Paper tackles uncertainty in GNNs for graph data.
problem Uncertainty in GNNs' predictions for graph data.
method CF-T2NN, tensor decomposition, topological learning.
result CF-T2NN improves reliability and interpretability of GNN outcomes.
Statistical uncertainty of different filtration techniques for market network analysis is studied. Two measures of statistical uncertainty are discussed. One is based on conditional risk for multiple decision statistical procedures and another one is based on average fraction of errors. It is shown that for some import…
New framework models uncertainty in classification debates.
problem Weak interpretability of existing uncertainty quantification methods.
method Courtroom analogy and Mixture of Dirichlet Experts (MoDEX) model.
result MoDEX achieves state-of-the-art uncertainty quantification performance.
Selective prediction framework reduces errors in molecular structure identification from MS/MS.
problem High-stakes applications require reliable molecular structure identification from MS/MS data.
method Selective prediction framework using risk-coverage tradeoff and uncertainty quantification.
result First-order confidence measures and retrieval-level aleatoric uncertainty achieve strong risk-coverage tradeoffs.
Bayesian method for semi-structured models accounts for both types of uncertainty.
problem Lack of work on epistemic uncertainty in semi-structured regression models.
method Bayesian approximation with subspace inference for joint posterior sampling.
result Validated approach recovers structured effect posteriors and approaches full-space posterior.
New method handles structural uncertainty in graphs better than existing models.
problem Handling heterophily and structural noise in semi-supervised learning on graphs.
method Sparse signed message passing network that models a posterior distribution over signed adjacency matrices.
result Our method outperforms strong baseline models on heterophilic benchmarks under both synthetic and real-world structural noise.
Learning probability distributions on the weights of neural networks (NNs) has recently proven beneficial in many applications. Bayesian methods, such as Stein variational gradient descent (SVGD), offer an elegant framework to reason about NN model uncertainty. However, by assuming independent Gaussian priors for the i…
Structured credal learning separates covariate shift and label disagreement.
problem Uncertainty in real-world learning tasks due to covariate shift and noisy labels.
method Introduces a structured credal learning framework that explicitly separates these sources.
result Geometric bounds and decomposition reveal how covariate shifts affect label disagreement contributions.
Single-pass method estimates neural network uncertainty.
problem Uncertainty estimation in deep learning requires multiple passes.
method Probabilistic reasoning over neural network depths.
result Single forward pass for uncertainty estimation.
Survey on uncertainty in ML and DL, covering sources, quantification, and decision-making.
problem Understanding and quantifying uncertainty in ML and DL for risk-sensitive applications.
method Structured review of literature, categorizing uncertainty, assessing uncertainty quantification techniques.
result Broadened scope of uncertainty discussion and updated DL uncertainty quantification methods.
Study on the structure of classifier boundaries in DNA sequencing.
problem Understanding the structure of boundaries in a Bayes classifier for DNA sequencing.
method Examined the structure of the boundary in a Bayes classifier applied to DNA sequencing data. Introduced a new measure of uncertainty, Neighbor Similarity.
result The boundary is large and complex, and Neighbor Similarity effectively measures classifier uncertainty.
In this paper, we study term structure movements in the spirit of Heath, Jarrow, and Morton [Econometrica 60(1), 77-105] under volatility uncertainty. We model the instantaneous forward rate as a diffusion process driven by a G-Brownian motion. The G-Brownian motion represents the uncertainty about the volatility. With…
Method quantifies uncertainties in complex MRF models.
problem Uncertainties in MRF predictions due to data, modeling, and approximations.
method Information-based uncertainty quantification using MRF graphical structure.
result Tight bounds on predictions for quantities of interest in MRFs.
The paper develops scalable variational inference for Bayesian neural networks under model and parameter uncertainty.
problem Combining structural and parameter uncertainties in scalable Bayesian neural networks.
method Adapted variational inference with reparametrization for model space constraints.
result Comparable accuracy with sparse inference compared to ordinary BNNs.
Study finds market inefficiencies vary by time scale, with news uncertainty key.
problem Evaluating scale-dependent informational efficiency of stock markets.
method Tensor-eigenvalue-based Financial Chaos Index, Granger causality, network analysis.
result Semi-strong form of EMH rejected at daily frequency, but not at monthly.
We study the Hull-White model for the term structure of interest rates in the presence of volatility uncertainty. The uncertainty about the volatility is represented by a set of beliefs, which naturally leads to a sublinear expectation and a G-Brownian motion. The main question in this setting is how to find an arbitra…
Unified taxonomy for ML uncertainty in physics, validated.
problem Uncertainty quantification in machine learning for physics.
method Unified taxonomy, principled validation tools.
result Illustrated validation tools with examples.
Generative models improve digital twins for structures with uncertainties.
problem Uncertainty in structural modelling limits deterministic models.
method Two types of generative models: physics-based SFE and data-driven cGANs.
result Data-driven cGANs outperform physics-based models in nonlinear structures.
Improved Bayesian uncertainty quantification using variational bagging.
problem Inefficient and underestimating uncertainty in mean-field variational Bayes.
method Integrates bagging with variational Bayes for improved inference.
result Bagged variational posterior provides proper uncertainty quantification.
Overparametrized neural networks retain significant epistemic uncertainty even with sufficient data.
problem Epistemic uncertainty in overparametrized neural networks persists despite model identifiability.
method Analysis of non-identifiability and characterization of residual uncertainty in one-hidden-layer ReLU networks.
result Substantial parameter uncertainty remains even when the underlying function is fully identified.
This paper improves deep learning by integrating Bayesian inference into network structure learning.
problem Bayesian inference in high-dimensional, over-parameterized neural networks.
method Developed an efficient stochastic variational inference approach to learn both network structure and weights.
result Empirically, the method exhibits competitive predictive performance and preserves Bayesian benefits.
A new framework models uncertainty in structured temporal data using SDEs and neural networks.
problem Uncertainty quantification in machine learning applications involving structured and temporal data.
method Integrates stochastic differential equations (SDEs) with deep generative models in a variational autoencoder framework.
result Improves uncertainty quantification in machine learning applications involving structured and temporal data.
Existing methods for structure discovery in time series data construct interpretable, compositional kernels for Gaussian process regression models. While the learned Gaussian process model provides posterior mean and variance estimates, typically the structure is learned via a greedy optimization procedure. This restri…
Deep learning uses layers of transformations to predict structured data with uncertainty.
problem Predicting structured high-dimensional data efficiently and with uncertainty.
method Applying layers of semi-affine input transformations to find features for probabilistic statistical methods.
result Achieves scalable prediction rules with uncertainty quantification and feature selection.
Bayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using Bayesian approach: Parameter and prediction uncertainty become easily available, fac…
UM-GNN improves GNN robustness against poisoning attacks.
problem Vulnerability of GNNs to poisoning attacks.
method UM-GNN uses epistemic uncertainties from message passing to build a surrogate predictor.
result UM-GNN achieves significantly improved robustness against poisoning attacks.
Bayesian parametric matrix models provide uncertainty quantification for spectral learning.
problem Uncertainty quantification in spectral learning for safety-critical applications.
method Bayesian parametric matrix models (B-PMMs) that extend PMMs to provide uncertainty estimates.
result B-PMMs achieve exceptional uncertainty calibration (ECE < 0.05) while maintaining favorable scaling.
Develops a method to estimate uncertainty for group-level recommendations in matrix completion.
problem Uncertainty estimation for group-level recommendations in matrix completion.
method Structured conformal inference method combining any matrix completion algorithm.
result Stronger group-level guarantees through structured calibration.
New method learns graph structure and uncertainty from data.
problem Learning latent graph structures and their uncertainty from data.
method Proposes a sampling-based method to learn latent graph structure and uncertainty simultaneously.
result Proves that suitable loss functions on stochastic model outputs solve both learning latent graph structure and achieving optimal predictions.
This paper uses a diffusion model to forecast electrical loads with uncertainty.
problem Uncertainties in electrical load forecasting due to renewable energy and external events.
method Diffusion-based Seq2Seq structure for epistemic uncertainty and robust additive Cauchy distribution for aleatoric uncertainty.
result Ability to separate and quantify both types of uncertainties in load forecasting.
This research develops efficient surrogate models for predicting crack growth in metal structures.
problem Accurately predicting crack growth in metal structures under uncertainty.
method Employing Gaussian Process (GP) regression models for latent variable modeling to create probabilistic surrogate models.
result Surrogate models successfully encode material and load-related uncertainties in stochastic crack growth processes.
Hybrid model integrates GATv2 and geostatistics for better spatial prediction and uncertainty.
problem Accurate spatial prediction and uncertainty quantification in epidemiology and risk analysis.
method Integrates Graph Attention Network (GATv2) with model-based geostatistics (MBG) to capture relational and spatial dependencies.
result Hybrid model improves predictive accuracy and uncertainty quantification compared to standalone models.
Bayesian method identifies dynamical models with uncertainty quantification.
problem Uncertainty in selecting governing equations for dynamical systems.
method Bayesian sparse identification with model averaging.
result Accurately recovers sparse interaction structures with uncertainty quantification.
Extends fractional Lp uncertainty principles with extremizers and stability results.
problem Investigating uncertainty principles in fractional Lp settings. method Analyzing the fractional Schrödinger equation to find extremal functions and sharp constants.
result Proves stability of extremizers for fractional uncertainty inequalities.