Papers Uncertainty Quantification
“Uncertainty Quantification” 태그가 달린 논문 2,366편 · 필터 해제
Distributional Reinforcement Learning on Path-dependent Options
We reinterpret and propose a framework for pricing path-dependent financial derivatives by estimating the full distribution of payoffs using Distributional Reinforcement Learning (DistRL). Unlike traditional methods that…
Distributional Reinforcement Learningreinforcement-learningReinforcement LearningUncertainty QuantificationInterpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
Tensor Network (TN) Kernel Machines speed up model learning by representing parameters as low-rank TNs, reducing computation and memory use. However, most TN-based Kernel methods are deterministic and ignore parameter un…
feature selectionUncertainty QuantificationVariational InferenceJoint space-time wind field data extrapolation and uncertainty quantification using nonparametric Bayesian dictionary learning
A methodology is developed, based on nonparametric Bayesian dictionary learning, for joint space-time wind field data extrapolation and estimation of related statistics by relying on limited/incomplete measurements. Spec…
Dictionary LearningUncertainty QuantificationA Risk-Aware Adaptive Robust MPC with Learned Uncertainty Quantification
Solving chance-constrained optimal control problems for systems subject to non-stationary uncertainties is a significant challenge.Conventional robust model predictive control (MPC) often yields excessive conservatism by…
Active LearningModel Predictive ControlUncertainty QuantificationFrom Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion
Quantitative remote sensing inversion aims to estimate continuous surface variables-such as biomass, vegetation indices, and evapotranspiration-from satellite observations, supporting applications in ecosystem monitoring…
Domain GeneralizationUncertainty QuantificationUncertainty Quantification for Motor Imagery BCI -- Machine Learning vs. Deep Learning
Brain-computer interfaces (BCIs) turn brain signals into functionally useful output, but they are not always accurate. A good Machine Learning classifier should be able to indicate how confident it is about a given class…
Deep LearningMotor ImageryUncertainty QuantificationUQLM: A Python Package for Uncertainty Quantification in Large Language Models
Hallucinations, defined as instances where Large Language Models (LLMs) generate false or misleading content, pose a significant challenge that impacts the safety and trust of downstream applications. We introduce UQLM, …
HallucinationUncertainty QuantificationEstimating prevalence with precision and accuracy
Unlike classification, whose goal is to estimate the class of each data point in a dataset, prevalence estimation or quantification is a task that aims to estimate the distribution of classes in a dataset. The two main t…
Uncertainty QuantificationNRSeg: Noise-Resilient Learning for BEV Semantic Segmentation via Driving World Models
Birds' Eye View (BEV) semantic segmentation is an indispensable perception task in end-to-end autonomous driving systems. Unsupervised and semi-supervised learning for BEV tasks, as pivotal for real-world applications, u…
Autonomous DrivingBEV SegmentationSegmentationSemantic Segmentation+1Deterministic Object Pose Confidence Region Estimation
6D pose confidence region estimation has emerged as a critical direction, aiming to perform uncertainty quantification for assessing the reliability of estimated poses. However, current sampling-based approach suffers fr…
Conformal PredictionObjectPose EstimationUncertainty QuantificationForecasting Geopolitical Events with a Sparse Temporal Fusion Transformer and Gaussian Process Hybrid: A Case Study in Middle Eastern and U.S. Conflict Dynamics
Forecasting geopolitical conflict from data sources like the Global Database of Events, Language, and Tone (GDELT) is a critical challenge for national security. The inherent sparsity, burstiness, and overdispersion of s…
Uncertainty QuantificationScalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference
Despite their widespread use, large language models (LLMs) are known to hallucinate incorrect information and be poorly calibrated. This makes the uncertainty quantification of these models of critical importance, especi…
Bayesian InferenceUncertainty QuantificationVariational InferenceCOIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees
Uncertainty quantification (UQ) for foundation models is essential to identify and mitigate potential hallucinations in automatically generated text. However, heuristic UQ approaches lack formal guarantees for key metric…
Conformal PredictionQuestion AnsweringText GenerationUncertainty QuantificationStructural System Identification via Validation and Adaptation
Estimating the governing equation parameter values is essential for integrating experimental data with scientific theory to understand, validate, and predict the dynamics of complex systems. In this work, we propose a ne…
parameter estimationUncertainty QuantificationLatent-space Field Tension for Astrophysical Component Detection An application to X-ray imaging
Modern observatories are designed to deliver increasingly detailed views of astrophysical signals. To fully realize the potential of these observations, principled data-analysis methods are required to effectively separa…
Uncertainty QuantificationUncertainty-Aware Machine-Learning Framework for Predicting Dislocation Plasticity and Stress-Strain Response in FCC Alloys
Machine learning has significantly advanced the understanding and application of structural materials, with an increasing emphasis on integrating existing data and quantifying uncertainties in predictive modeling. This s…
Uncertainty QuantificationA Framework for Uncertainty Quantification Based on Nearest Neighbors Across Layers
Neural Networks have high accuracy in solving problems where it is difficult to detect patterns or create a logical model. However, these algorithms sometimes return wrong solutions, which become problematic in high-risk…
Autonomous DrivingMedical DiagnosisUncertainty QuantificationGNN's Uncertainty Quantification using Self-Distillation
Graph Neural Networks (GNNs) have shown remarkable performance in the healthcare domain. However, what remained challenging is quantifying the predictive uncertainty of GNNs, which is an important aspect of trustworthine…
Knowledge DistillationUncertainty QuantificationConsensus-Driven Uncertainty for Robotic Grasping based on RGB Perception
Deep object pose estimators are notoriously overconfident. A grasping agent that both estimates the 6-DoF pose of a target object and predicts the uncertainty of its own estimate could avoid task failure by choosing not …
ObjectPose EstimationRobotic GraspingUncertainty QuantificationWhen Can We Reuse a Calibration Set for Multiple Conformal Predictions?
Reliable uncertainty quantification is crucial for the trustworthiness of machine learning applications. Inductive Conformal Prediction (ICP) offers a distribution-free framework for generating prediction sets or interva…
Conformal PredictionPredictionUncertainty Quantification