Improved neural network predicts tropical storm trajectories and Bayesian intervals.
problem Accurately predicting the trajectories of tropical storms to prevent damage.
method Developed an improved RNN model with dropout to predict Bayesian intervals.
result Neural network dropout values significantly affect prediction accuracy and intervals.
Bayesian method for estimating ATE with robustness to model misspecification.
problem Estimating average treatment effects under unconfoundedness.
method Double robust Bayesian inference using adjusted prior and posterior distributions.
result Bayesian credible sets form asymptotically exact confidence intervals.
Paper compares Bayesian and de-biased estimators for low-rank matrix completion.
problem Predict missing entries in partially observed matrices.
method Bayesian and de-biased estimators comparison.
result De-biased estimator performs similarly to Bayesian estimators but is more stable and can outperform in small samples.
This work reviews and evaluates methods for predicting prediction intervals in regression problems.
problem Calibration of prediction intervals in regression problems.
method Four classes of methods: Bayesian, ensemble, direct interval estimation, and conformal prediction.
result Conformal prediction can be used as a general calibration procedure.
Bayesian inference engines improve density estimation accuracy and scalability.
problem Constructing accurate and scalable probability density functions.
method Bayesian inference engines (no-U-turn sampling and expectation propagation) with binning strategy.
result Density estimates have excellent comparative performance and scale well to large sample sizes.
Unified Bayesian framework improves clinical trial hypothesis testing.
problem Lack of transparency and inability to quantify evidence in traditional P-values.
method Interval null hypothesis framework combined with Bayes factor-based tests.
result Bayesian interval hypothesis testing ensures frequentist error control and interpretability.
Bayesian method improves estimation of unseen species.
problem Estimating unseen species in biological and physical sciences.
method Bayesian nonparametric approach with Pitman-Yor prior and Gaussian credible intervals.
result Improves asymptotic credible intervals for unseen species estimation.
Optimism about the poorly understood states and actions is the main driving force of exploration for many provably-efficient reinforcement learning algorithms. We propose optimism in the face of sensible value functions (OFVF)- a novel data-driven Bayesian algorithm to constructing Plausibility sets for MDPs to explore…
Bayesian meta learning improves uncertainty quantification in regression.
problem Trusting uncertainty quantification in Bayesian regression.
method Trust-Bayes framework for Bayesian meta learning, optimizing for trustworthy uncertainty quantification.
result Lower bounds and sample complexity for trustworthy uncertainty quantification are characterized.
Methods for reasoning under uncertainty are a key building block of accurate and reliable machine learning systems. Bayesian methods provide a general framework to quantify uncertainty. However, because of model misspecification and the use of approximate inference, Bayesian uncertainty estimates are often inaccurate -…
This paper studies uncertainty quantification in deep spatiotemporal forecasting.
problem Uncertainty quantification in deep spatiotemporal forecasting models.
method Analysis of UQ methods from Bayesian and frequentist perspectives, including statistical decision theory.
result Different UQ methods have different strengths and weaknesses, with Bayesian methods being more robust in mean prediction and frequentist methods providing more extensive coverage.
Given discrete time observations over a fixed time interval, we study a nonparametric Bayesian approach to estimation of the volatility coefficient of a stochastic differential equation. We postulate a histogram-type prior on the volatility with piecewise constant realisations on bins forming a partition of the time in…
Adapts Gaussian process surrogate evaluation with conformal prediction for better coverage guarantees.
problem Uncertainty quantification and model specification issues in Gaussian process surrogate models.
method Adaptive cross-conformal prediction intervals using posterior standard deviation weighting.
result Conformal prediction intervals provide significant correlation with surrogate model error and frequentist coverage guarantees.
BIGUE algorithm provides credible intervals for hyperbolic network embeddings.
problem Uncertainty in hyperbolic network embeddings.
method Markov chain Monte Carlo (MCMC) algorithm for Bayesian hyperbolic random graph model.
result Samples from the posterior distribution provide credible intervals for hyperbolic coordinates and network properties.
Develops a Bayesian method for causal inference with partly censored time-to-event data.
problem Estimating causal effects with unobserved confounders and measurement errors in partly censored time-to-event data.
method Semiparametric Bayesian instrumental variable analysis using a two-stage Dirichlet process mixture model.
result The proposed method outperforms competing methods in simulations and real-world data analysis.
Methods for prediction and tolerance intervals in non-normal models.
problem Constructing prediction and tolerance intervals for non-normal data.
method Two approaches: pivotal quantity approximation and confidence interval for mean.
result Intuitive, simple, efficient methods with proper operating characteristics.
New algorithm for efficient prediction intervals in neural networks.
problem Challenges in estimating uncertainty in neural network predictions.
method Applies matrix sketching to approximate Jacobian matrix for efficient uncertainty estimation.
result Produces approximate prediction intervals with competitive performance.
EPICSCORE improves conformal scores by explicitly accounting for epistemic uncertainty.
problem Overconfident predictions in data-sparse regions due to lack of epistemic uncertainty.
method Model-agnostic approach using Bayesian techniques like Gaussian Processes, Dropout, and Regression Trees.
result Enhanced predictive intervals that adaptively expand in sparse data regions and maintain compact intervals in abundant data.
Discriminative jackknife estimates deep learning uncertainty.
problem Quantifying uncertainty in deep learning models.
method Discriminative jackknife using influence functions of loss.
result DJ satisfies frequentist coverage and discriminative accuracy.
The upsilon distribution, the sum of independent chi random variates and a normal, is introduced. As a special case, the upsilon distribution includes Lecoutre's lambda-prime distribution. The upsilon distribution finds application in Frequentist inference on the Sharpe ratio, including hypothesis tests on independent …
Bayesian framework improves LLM evaluation stability and transparency.
problem Pass@k and avg@N are unstable and misleading for LLMs.
method Bayesian evaluation with posterior estimates and credible intervals.
result Posterior-based evaluation yields stable and transparent rankings.
INNs produce interval-valued uncertainty scores for DNNs.
problem Uncertainty quantification in deep neural networks.
method Data-driven interval propagating network using interval arithmetic.
result INNs produce sensible lower and upper bounds for prediction error.
This work develops rigorous theoretical basis for the fact that deep Bayesian neural network (BNN) is an effective tool for high-dimensional variable selection with rigorous uncertainty quantification. We develop new Bayesian non-parametric theorems to show that a properly configured deep BNN (1) learns the variable im…
Bayesian Transformer improves probabilistic load forecasting with calibrated uncertainty estimates.
problem Overconfident point predictions from deep learning models fail under extreme weather distributional shifts.
method Integrates three uncertainty mechanisms: MC Dropout, variational layers, and stochastic attention.
result Achieves state-of-the-art performance with CRPS of 0.0289 and 90% PICP across various horizons.
Stochastic gradient Langevin dynamics (SGLD) is a computationally efficient sampler for Bayesian posterior inference given a large scale dataset. Although SGLD is designed for unbounded random variables, many practical models incorporate variables with boundaries such as non-negative ones or those in a finite interval.…
Complex activity recognition is challenging due to the inherent uncertainty and diversity of performing a complex activity. Normally, each instance of a complex activity has its own configuration of atomic actions and their temporal dependencies. We propose in this paper an atomic action-based Bayesian model that const…
Paper extends Bayes Theorem for interval probability estimates.
problem Real-world input probabilities are often interval estimates, not precise.
method Developed IT2 version of Bayes Theorem and a novel algorithm for encoding intervals.
result Conservative method avoids invalid output results from inconsistent input.
This paper computes exact posterior distributions of mixture weights in hierarchical Bayesian models.
problem Uncertainty in class membership or data-generating processes in heterogeneous data.
method Exact marginalization of mixture weights using dynamic programming and FFT for two components, and joint dynamic program for K >= 3 components.
result Exact posterior distributions of mixture weights are finite mixtures of Beta distributions, providing credible intervals and per-observation local false-discovery rates.
Study probabilistic safety of BNNs under adversarial attacks.
problem Evaluate vulnerability of BNNs to adversarial attacks.
method Relaxation techniques from non-convex optimization to compute probabilistic safety bounds.
result Certify probabilistic safety of BNNs with millions of parameters.
EBLIME enhances model explanations using Bayesian ridge regression.
problem Improving model explanations for black-box machine learning models.
method EBLIME uses Bayesian ridge regression to explain feature importance.
result EBLIME provides more intuitive and accurate feature importance rankings.
SA-BCP combines long-term and local evidence for efficient, adaptive online prediction.
problem Balancing fast adaptation and stable coverage in online prediction.
method State-Adaptive Bayesian Conformal Prediction (SA-BCP) using gated convex combination of temporal inertia and spatial evidence.
result SA-BCP achieves at-or-above-nominal coverage with substantially sharper intervals compared to discounted Bayesian CP.
Motivated by the growing popularity of variants of the Wasserstein distance in statistics and machine learning, we study statistical inference for the Sliced Wasserstein distance--an easily computable variant of the Wasserstein distance. Specifically, we construct confidence intervals for the Sliced Wasserstein distanc…
This paper develops a new method for online density estimation from noisy data.
problem Estimating probability density function from noisy streaming data.
method Quasi-Bayesian sequential deconvolution using Newton's algorithm.
result Sequential deconvolution estimate fn with large sample asymptotic guarantees. Innovative inequalities for divergences with applications in PAC-Bayesian bounds and Monte Carlo.
problem Developing new inequalities for divergences.
method Introducing novel change of measure inequalities for f-divergences and α-divergences. result Applications in PAC-Bayesian bounds and Monte Carlo estimates.
Proposes PQ, a more precise Bayesian quantifier for prevalence estimation.
problem Uncertainty quantification in prevalence estimation.
method Bayesian quantification methods, focusing on precision and coverage.
result PQ provides more precise and well-calibrated uncertainty quantification.
Study uses BNs to predict cryptocurrency prices, improving accuracy with discretisation.
problem Predicting price movements in volatile cryptocurrency markets.
method Discretisation-aware Bayesian Networks with three methods and multiple bin counts.
result Equal interval with two bins provides best predictive performance.
Motivation: Recent advances in technology for brain imaging and high-throughput genotyping have motivated studies examining the influence of genetic variation on brain structure. Wang et al. (Bioinformatics, 2012) have developed an approach for the analysis of imaging genomic studies using penalized multi-task regressi…
Bayesian framework predicts aerodynamic uncertainty from sparse measurements.
problem Calibrating aerodynamic models with sparse and uncertain measurements.
method Bayesian latent Gaussian process for surrogate model calibration.
result Calibrated surrogate model accurately predicts aerodynamic uncertainty.
Adaptive truncation improves privacy in online Bayesian estimation.
problem Ensuring privacy in online Bayesian estimation of a static parameter.
method Sequential Monte Carlo, adaptive truncation, Thompson sampling.
result Adaptive truncation reduces privacy-preserving noise, enabling more accurate estimation.
DABS uses a policy network to select experiments in high-dimensional design spaces.
problem Adaptive factorial screening in high-dimensional discrete design spaces.
method DABS learns a policy network offline to sequentially select experiments, incorporating sparsity and interactions via a spike-and-slab prior.
result DABS achieves superior accuracy and scalability over classical and Bayesian baselines under tight experimental budgets.
Two models predict net loan losses using Bayesian and frequentist regression.
problem Predicting net loan losses using financial and sociological data.
method Bayesian and frequentist regression analysis.
result Improved understanding of net loan loss relationships.
New method for accurate uncertainty estimation in deep learning predictions.
problem Insufficient methods for assessing prediction uncertainty in deep learning.
method Valid non-parametric bootstrap method for deep neural networks.
result Accurate confidence intervals and simultaneous confidence bands for survival data.
Unified Bayesian-AI framework improves epidemiological risk prediction and uncertainty quantification.
problem Lack of calibrated uncertainty in machine learning models for epidemiology.
method Combines Bayesian prediction with Bayesian hyperparameter optimization using logistic regression and Gaussian-process Bayesian optimization.
result Unified Bayesian-AI framework provides reliable coverage and improved calibration, enhancing epidemiological decision making.
Study evaluates quality of uncertainty estimates for neural networks.
problem Lack of principled assessment methods for evaluating uncertainty quality in deep learning.
method Statistical methods of frequentist interval coverage, interval width, and expected calibration error.
result Different UQ methods produce markedly different quality uncertainty estimates.
The paper improves Bayesian optimization by calibrating uncertainty estimates.
problem Improper uncertainty estimates in Bayesian optimization when data is non-stationary.
method Proposes online learning algorithms to maintain calibration on non-i.i.d. data and integrates them into Bayesian optimization.
result Calibrated Bayesian optimization converges to better optima in fewer steps.
The mass, or binding energy, is the basis property of the atomic nucleus. It determines its stability, and reaction and decay rates. Quantifying the nuclear binding is important for understanding the origin of elements in the universe. The astrophysical processes responsible for the nucleosynthesis in stars often take …
DDGM generates realistic ECG signals for clinical use.
problem Generating accurate ECG signals from noisy data.
method Bayesian ECG reconstruction using DDGM trained on healthy ECG data.
result DDGM successfully generates realistic ECG signals for clinical applications.
Bayesian principles improve neural additive models for better feature selection and uncertainty.
problem Lack of calibrated uncertainties and feature selection in neural additive models.
method Augmenting NAMs with Bayesian principles to provide credible intervals, feature selection, and interaction ranking.
result Improved performance on tabular datasets and real-world medical tasks.