New geometric metrics improve Bayesian optimization performance evaluation.
problem Current metrics lack geometric insights and cannot compare algorithms effectively.
method Proposed four geometric metrics: precision, recall, average degree, and average distance.
result Proposed metrics provide more detailed evaluation of Bayesian optimization.
We propose a novel classifier accuracy metric: the Bayesian Area Under the Receiver Operating Characteristic Curve (CBAUC). The method estimates the area under the ROC curve and is related to the recently proposed Bayesian Error Estimator. The metric can assess the quality of a classifier using only the training datase…
Design of experiments improves validation of biomolecular networks.
problem Efficiently validate non-machine learning designed biomolecular networks.
method Use Gaussian processes and Bayesian optimization to select experimental points.
result Developed a stopping criterion based on discrepancy metric and uncertainty.
Proposes a neural network loss function for better uncertainty estimation.
problem Challenges in estimating predictive uncertainty of neural networks.
method Bayesian Validation Metric (BVM) framework with ensemble learning.
result Competitive and robust uncertainty estimation on in-distribution and out-of-distribution data.
This paper presents an efficient Bayesian framework for solving nonlinear, high-dimensional model calibration problems. It is based on a Variational Bayesian formulation that aims at approximating the exact posterior by means of solving an optimization problem over an appropriately selected family of distributions. The…
Bayesian model compares ML algorithms on various datasets.
problem Comparing multiple machine learning algorithms across multiple datasets.
method Bayesian Bradley-Terry model, defining regions of practical equivalence (ROPE).
result Allows nuanced statements and ROPE definitions for algorithm comparison.
In a recent paper [1] we introduced the Fuzzy Bayesian Learning (FBL) paradigm where expert opinions can be encoded in the form of fuzzy rule bases and the hyper-parameters of the fuzzy sets can be learned from data using a Bayesian approach. The present paper extends this work for selecting the most appropriate rule b…
Hyperparameter optimization aims to find the optimal hyperparameter configuration of a machine learning model, which provides the best performance on a validation dataset. Manual search usually leads to get stuck in a local hyperparameter configuration, and heavily depends on human intuition and experience. A simple al…
A method for efficient CV estimates in Bayesian hierarchical models.
problem Computational infeasibility of cross-validation in Bayesian hierarchical regression models.
method Conditioning on variance-covariance parameters to transform CV into an optimization problem.
result Equivalent or improved predictive estimates compared to full cross-validation.
Bayesian approach improves car-following model calibration and validation with limited data.
problem Difficulties in calibrating car-following models using limited data for work zones.
method Bayesian programming for data analysis and parameter estimation.
result Bayesian methods enhance model calibration and validation accuracy.
This paper benchmarks Bayesian models' ability to estimate predictive correlations, especially for active learning.
problem Benchmarking how accurately Bayesian models estimate predictive correlations, especially in active learning.
method Considered transductive active learning as a benchmark, introduced meta-correlations and cross-normalized likelihoods.
result Meta-correlations and cross-normalized likelihoods can efficiently evaluate predictive correlations and are consistent with TAL performance.
Bayesian inference corrected for bias in high-dimensional models.
problem Bayesian inference for high-dimensional regression models often produces biased credible sets.
method Debiasing approach based on Bernstein-von Mises theorem.
result Frequentist validity of debiased Bayesian posterior.
Optimizes spectral density estimation for stationary and nonstationary processes.
problem Estimating spectral density of time series with complex structure.
method Optimally adaptive Bayesian spectral density estimation using smoothing spline covariance structure.
result Optimal eigendecomposition provides superior performance compared to alternative covariance functions.
OBSER framework infers sub-environments from objects, outperforming scene-based methods.
problem Zero-shot recognition of environments from object distributions.
method Bayesian framework using metric and self-supervised learning models to estimate object distributions in latent space.
result OBSER framework reliably performs inference in open-world and photorealistic environments, outperforming scene-based methods.
Bayesian UQ matches frequentist UQ for adaptively collected data.
problem Uncertainty quantification for adaptive data collection.
method Extends Bernstein-von Mises theorem to adaptively collected data.
result Bayesian UQ asymptotically matches Wald-type frequentist UQ.
PAC-Bayesian bounds for MLPs with cross entropy loss validated.
problem Generalization bounds for MLPs with cross entropy loss.
method Introduced probabilistic explanations and proved PAC-Bayesian bounds using ELBO.
result MLPs with cross entropy loss inherently guarantee PAC-Bayesian generalization bounds.
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.
Paper decomposes risk into aleatoric and epistemic uncertainties and generates predictive uncertainty measures.
problem Unclear relationships between various predictive uncertainty measures in literature.
method Bayesian estimation to decompose risk into aleatoric and epistemic uncertainties, generating different predictive uncertainty measures.
result Experimental validation confirms usefulness of derived predictive uncertainty measures for detecting out-of-distribution and misclassified instances.
Method removes misleading data to improve ML model accuracy.
problem Unhealthy fear of missing out on data leads to model instability and poor performance.
method Bayesian sequential selection method that identifies and selects critical information.
result Improves sample-wise error convergence and eliminates model instabilities.
Semiparametric Bayesian networks combine parametric and nonparametric models for flexible data analysis.
problem Combining the advantages of parametric and nonparametric models for flexible data analysis.
method Semiparametric Bayesian networks combining parametric and nonparametric conditional probability distributions. Modifications of two algorithms for structure learning from data.
result Accurately learns the combination of parametric and nonparametric components, comparable to state-of-the-art methods.
In this work, we empirically explore the question: how can we assess the quality of samples from some target distribution? We assume that the samples are provided by some valid Monte Carlo procedure, so we are guaranteed that the collection of samples will asymptotically approximate the true distribution. Most current …
Bayesian online learning algorithm for one-pass data, achieving frequentist validity and uncertainty quantification.
problem Theoretical limitations in Bayesian online learning, especially in the one-pass setting.
method Proposed a new Bayesian online learning algorithm with a warm-start phase for the one-pass regime, establishing convergence rates and valid uncertainty quantification.
result The sequentially updated posterior attains optimal convergence rates and valid uncertainty quantification without diverging mini-batch sample sizes.
Proposes a new method to improve Bayesian computation accuracy using flexible classification.
problem Bayesian computations accuracy check using rank-based simulation-based calibration has limitations.
method Replaces marginal rank test with a flexible classification approach that learns from data.
result Improves statistical power and provides an interpretable divergence measure of miscalibration.
BARMPy offers a Python package for Bayesian Additive Regression Models.
problem Making complex Bayesian models accessible to machine learning practitioners.
method Object-oriented design compatible with SciKit-Learn, documentation and tutorial provided.
result Ease of use and compatibility with existing machine learning tools.
The future predictive performance of a Bayesian model can be estimated using Bayesian cross-validation. In this article, we consider Gaussian latent variable models where the integration over the latent values is approximated using the Laplace method or expectation propagation (EP). We study the properties of several B…
Bayesian model averaging under predictor redundancy
problem Reporting Bayesian model averaging posterior without changing the Bayesian target
method Using hard or soft regions of support space
result Region reports often give shorter and clearer summaries while preserving the main posterior information
Bayesian variational inference improves medical image segmentation confidence.
problem Improving interpretability and confidence in deep learning models for medical image segmentation.
method Encoder-decoder architecture based on variational inference for segmenting brain tumor images.
result The model segments brain tumors with both aleatoric and epistemic uncertainty.
A new method improves robustness and efficiency of Bayesian LOO-CV.
problem Computational expense and unreliability of classical LOO-CV in high-dimensional Bayesian models.
method Proposes a mixture estimator to compute Bayesian LOO-CV criteria with finite asymptotic variance.
result Improved robustness and efficiency in high-dimensional problems.
Four geometries govern sequential and distribution-free inference.
problem Sequential and distribution-free inference challenges.
method Four distinct admissibility geometries.
result Four classes of admissible procedures are pairwise non-nested.
Bayesian method discovers PDEs with variable coefficients robustly.
problem Discovering PDEs from noisy data is challenging.
method Bayesian sparse learning with tBGL-SS and Gibbs sampler.
result Method enhances robustness and model selection criteria.
New framework improves experimental design using integral probability metrics.
problem Challenges in Bayesian Optimal Experimental Design (BOED) with KL divergence.
method Integrates integral probability metrics (IPMs) for stability and flexibility.
result IPM-based designs yield more robust and accurate credible sets.
A new model predicts multivariate regression using similarities to data points.
problem Complex, high-dimensional input-output relationships.
method Bayesian mixture-of-experts with conditional Gaussian mixtures and variational Bayes.
result Outperforms competitors in high-dimensional settings.
Introduces BCVI, a Bayesian cluster validity index for better cluster selection.
problem Selecting the optimal number of clusters in clustering algorithms.
method Develops BCVI using Dirichlet or generalized Dirichlet priors, evaluated against existing indices.
result BCVI offers clear advantages in scenarios where user expertise is valuable.
Bayesian method improves SOM training for dynamic data.
problem Training Self Organizing Maps (SOM) for non-stationary data.
method Variational Bayesian approach with adaptive neighborhood radius.
result Method outperforms other adaptive methods in high dimensions.
Bayesian approach fixes overconfidence in ReLU networks, even slightly.
problem Overconfidence in ReLU networks far from training data.
method Theoretical analysis of approximate Gaussian distributions on ReLU weights, and empirical validation.
result Even a simplistic Bayesian approximation fixes overconfidence issues.
New method optimizes costly evaluations in Bayesian optimization.
problem Costly evaluations in BO methods, especially in hyperparameter tuning.
method Formulated as a CMDP, developed rollout approximation for optimal policy.
result Improved cost efficiency over standard BO methods.
Usually one compares the accuracy of two competing classifiers via null hypothesis significance tests (nhst). Yet the nhst tests suffer from important shortcomings, which can be overcome by switching to Bayesian hypothesis testing. We propose a Bayesian hierarchical model which jointly analyzes the cross-validation res…
Several structure learning algorithms have been proposed towards discovering causal or Bayesian Network (BN) graphs. The validity of these algorithms tends to be evaluated by assessing the relationship between the learnt and the ground truth graph. However, there is no agreed scoring metric to determine this relationsh…
Distance metric learning is an important component for many tasks, such as statistical classification and content-based image retrieval. Existing approaches for learning distance metrics from pairwise constraints typically suffer from two major problems. First, most algorithms only offer point estimation of the distanc…
In Bayesian statistics, the marginal likelihood, also known as the evidence, is used to evaluate model fit as it quantifies the joint probability of the data under the prior. In contrast, non-Bayesian models are typically compared using cross-validation on held-out data, either through k-fold partitioning or leave-$p…
Bayesian Optimisation (BO) refers to a class of methods for global optimisation of a function f which is only accessible via point evaluations. It is typically used in settings where f is expensive to evaluate. A common use case for BO in machine learning is model selection, where it is not possible to analytically…
Bayesian uncertainty quantification is flawed, according to new research.
problem Flawed interpretation of Bayesian uncertainty quantification.
method Discussion of Bayesian updating and optimization-based perspective, proposing measures of quality.
result Bayesian uncertainty quantification is not coherent with optimization-based perspective.
Proposes a new cross-validation method to estimate model performance.
problem The standard cross-validation method does not accurately estimate the performance of the recommended model.
method Develops a new random-effects model framework to improve naive cross-validation estimators.
result Proposed estimators outperform conventional and naive methods in estimating model performance.
Differentially private hyperparameter tuning improves privacy in machine learning.
problem Hyperparameter tuning leaks private information through selected configurations.
method Local Bayesian optimization using Gaussian Process surrogate for private gradient approximation.
result DP-GIBO converges to locally optimal hyperparameters with polynomial dimensional dependence.
Bayesian neural networks can be simplified by parameterizing weights as rank-r matrices, reducing parameter count and improving performance.
problem High parameter count in standard Bayesian neural networks.
method Parameterize weights as W=ABop with A∈Rmimesr, B∈Rnimesr, inducing a singular posterior. result PAC-Bayes generalization bounds and loss bounds show improved performance with fewer parameters.
The paper establishes conditions for Bayesian consistency in supremum metric.
problem Ensuring Bayesian consistency in the supremum metric.
method Using a triangle inequality and weak convergence, the paper establishes conditions for Bayesian consistency.
result Demonstrates supremum consistency with weaker conditions than previously used.
Bayesian framework forecasts financial tail risks using realized volatility and nonlinear thresholds.
problem Forecasting financial tail risks using realized volatility and nonlinear thresholds.
method Bayesian Markov Chain Monte Carlo method for model estimation; nonlinear threshold regression specification.
result The proposed framework produces competitive tail risk forecasts compared to GARCH and Realized-GARCH models.
Bayesian deep learning improves out-of-distribution detection but not always.
problem Improving the reliability of deep learning models in uncertain or novel data.
method Validation of likelihood-based Bayesian models for out-of-distribution detection.
result Bayesian deep learning models can marginally outperform conventional neural networks in certain conditions.