arXiv research

Study wSAA for contextual decisions, improving uncertainty quantification under computational constraints.

New CAGP-GS framework improves Gaussian process uncertainty quantification.

This work tackles uncertainty quantification in tomography reconstruction.

New method improves uncertainty quantification for large batch sizes and misspecified models.

Paper introduces variational inference for Bayesian inverse problems with gamma hyperpriors.

Paper analyzes iterates in high-dimensional linear models and proposes estimators for their generalization error.

We introduce Fisher consistency in the sense of unbiasedness as a desirable property for estimators of class prior probabilities. Lack of Fisher consistency could be used as a criterion to dismiss estimators that are unlikely to deliver precise estimates in test datasets under prior probability and more general dataset…

ADS explains object differences by quantifying and removing underlying properties.

Bayesian Deep Noise Neural Network (B-DeepNoise) estimates predictive densities and uncertainty.

Bayesian neural networks learn graph structure with interpretable parameters.

Bayesian ptychography method reduces overlap for faster imaging.

PET image reconstruction is challenging due to the ill-poseness of the inverse problem and limited number of detected photons. Recently deep neural networks have been widely and successfully used in computer vision tasks and attracted growing interests in medical imaging. In this work, we trained a deep residual convol…

Unified framework for arbitrary conditional inference using AI and Bayesian methods.

This paper proposes a novel uncertainty quantification framework for computationally demanding systems characterized by a large vector of non-Gaussian uncertainties. It combines state-of-the-art techniques in advanced Monte Carlo sampling with Bayesian formulations. The key departure from existing works is the use of i…

Paper proposes an efficient online Newton method with Nesterov's acceleration for streaming data.

Efficient method for tensor linear form inference with noisy incomplete data.

Modern statistical inference tasks often require iterative optimization methods to compute the solution. Convergence analysis from an optimization viewpoint only informs us how well the solution is approximated numerically but overlooks the sampling nature of the data. In contrast, recognizing the randomness in the dat…

BBNN improves neural network accuracy and uncertainty quantification.

Spectral clustering algorithms typically require a priori selection of input parameters such as the number of clusters, a scaling parameter for the affinity measure, or ranges of these values for parameter tuning. Despite efforts for automating the process of spectral clustering, the task of grouping data in multi-scal…

Coherent uncertainty quantification is a key strength of Bayesian methods. But modern algorithms for approximate Bayesian posterior inference often sacrifice accurate posterior uncertainty estimation in the pursuit of scalability. This work shows that previous Bayesian coreset construction algorithms---which build a sm…

A new MCMC method combines low and high-fidelity models to reduce computation.

DIN framework directly models hydraulic conductivity and uncertainty.

Wasserstein gradient boosting predicts probability distributions for supervised learning.

HistNetQ improves quantification tasks by optimizing loss functions and eliminating label requirements.

Quantification is a supervised learning task that consists in predicting, given a set of classes C and a set D of unlabelled items, the prevalence (or relative frequency) p(c|D) of each class c in C. Quantification can in principle be solved by classifying all the unlabelled items and counting how many of them have bee…

Motivation: Ab initio protein docking represents a major challenge for optimizing a noisy and costly "black box"-like function in a high-dimensional space. Despite progress in this field, there is no docking method available for rigorous uncertainty quantification (UQ) of its solution quality (e.g. interface RMSD or iR…

Global optimization of expensive functions has important applications in physical and computer experiments. It is a challenging problem to develop efficient optimization scheme, because each function evaluation can be costly and the derivative information of the function is often not available. We propose a novel globa…

The paper develops statistical inference for gradient flows in optimization.

Deep Learning (DL) methods have emerged as one of the most powerful tools for functional approximation and prediction. While the representation properties of DL have been well studied, uncertainty quantification remains challenging and largely unexplored. Data augmentation techniques are a natural approach to provide u…

Fortuna simplifies uncertainty quantification in deep learning.

\emph{Sentiment Quantification} (i.e., the task of estimating the relative frequency of sentiment-related classes -- such as \textsf{Positive} and \textsf{Negative} -- in a set of unlabelled documents) is an important topic in sentiment analysis, as the study of sentiment-related quantities and trends across a populati…

A framework for navigating environments with spatially correlated obstacles and uncertain blockage status.

Bayesian meta learning improves uncertainty quantification in regression.

Developing stable and scalable probabilistic ODE solvers for stiff and high-dimensional problems.

New method uses TT approximations to solve HJB equations for efficient sampling.

LF-IBIS learns optimal policies online without explicit likelihood.

Generative models help make decisions under changing data distributions.

Scalable Bayesian LASSO using variational inference for large p and n.

Paper proposes a distributed sampling method for Bayesian inference.

Bayesian uncertainty quantification is flawed, according to new research.

New CLT for SGD in high-dimensional regression provides online inference.

A new KDE-based method improves multiclass quantification.

Geometry-aware KDE model improves multiclass quantification.

New bounds for SGD in high dimensions improve inference efficiency.

New GP-based method improves uncertainty quantification for causal functions.

Unified taxonomy for ML uncertainty in physics, validated.

Proposes PQ, a more precise Bayesian quantifier for prevalence estimation.

Bayesian neural network models improve uncertainty quantification in multivariate regression.