Proposes a method to quantify uncertainty in graph neural networks for node classification.
problem Uncertainty in graph neural networks for node classification.
method Bayesian uncertainty propagation (BUP) method embedding GNNs in a Bayesian framework.
result Demonstrates superior performance of the proposed method on benchmark datasets.
Scores measure certainty and doubt in classification predictions.
problem Quantitative uncertainty assessment in classification problems.
method Intuitive scores in Bayesian and frequentist frameworks.
result Measures assess and compare prediction quality and uncertainty.
Study improves theoretical understanding of Bayesian deep learning for classification tasks.
problem Theoretical gap in understanding Bayesian approaches in deep learning for classification.
method PAC-Bayes bounds techniques and Spike-and-Slab priors for sparse deep learning.
result Established non-asymptotic results for prediction error, achieving minimax optimal rates.
Graph Posterior Network improves uncertainty estimation for node classification in interdependent graphs.
problem Uncertainty quantification for non-independent node-level predictions in graphs.
method Derives axioms for expected predictive uncertainty, proposes Graph Posterior Network (GPN) which performs Bayesian posterior updates.
result GPN outperforms existing approaches for uncertainty estimation in semi-supervised node classification.
Binary classification models get more efficient predictive probabilities.
problem Computing predictive probabilities in Bayesian probit models is computationally challenging.
method Use of expectation propagation (EP) to find a closed-form expression for predictive probabilities.
result Closed-form predictive probabilities improve over existing methods.
Bayesian framework for online consensus prediction from expert feedback.
problem Online classification with expert consensus prediction, cost-effective.
method General Bayesian framework for dynamic expert consensus estimation.
result Demonstrated superior performance on large-scale crowdsourced datasets.
Combining Bayesian deep learning and split conformal prediction affects out-of-distribution coverage.
problem Improving out-of-distribution coverage in multiclass image classification.
method Combining Bayesian deep learning with split conformal prediction methods.
result Combining methods can reduce out-of-distribution coverage in some cases.
This research improves binary classification by balancing overfitting and generalization with a novel Bayesian approach.
problem Improving binary classification models to avoid overfitting and generalize well.
method Introduces a PAC-Bayes type learning rule with a balancing parameter λ to balance training error and KL divergence to a prior.
result A choice of λ ensures uniformly vanishing excess loss, even in the agnostic case, by under-regularizing or over-regularizing appropriately.
We introduce a method, KL-LIME, for explaining predictions of Bayesian predictive models by projecting the information in the predictive distribution locally to a simpler, interpretable explanation model. The proposed approach combines the recent Local Interpretable Model-agnostic Explanations (LIME) method with ideas …
A Bayesian approach to multilabel classification using tree-based models.
problem Challenges in multilabel classification due to complex label relationships and correlations.
method Bayesian Additive Regression Trees (BART) framework for modeling multilabel classification.
result Improved predictive performance compared to other models, including an oracle model.
Bayesian model improves classification performance with flexible uncertainty modeling.
problem Improving classification performance with flexible uncertainty modeling.
method Combines Gaussian process and Dirichlet process priors for latent function and link function, respectively.
result Outperforms standard logistic regression on simulated data.
Bayesian learning improves reliability of molecular predictions for hit compound discovery.
problem Improving reliability of machine learning predictions for virtual screening.
method Bayesian learning algorithms applied to graph neural networks.
result Bayesian learning leads to well-calibrated predictions and higher hit compound success.
New acquisition function improves batch Bayesian active learning.
problem BatchBALD conflates epistemic and aleatoric uncertainty, leading to suboptimal performance.
method Focus on predictive probabilities to separate epistemic uncertainty, leading to better performance and faster evaluation.
result The new acquisition function performs better and allows for larger batches.
Bayesian deep learning ensemble improves pneumonia diagnosis accuracy.
problem Manual, time-consuming pneumonia diagnosis with high inter-observer variability.
method Multi-level ensemble classification system using Bayesian Deep Learning.
result Accuracy of 98.06% in differentiating four pathologies.
Bayesian framework improves ML classification models' uncertainty estimates.
problem Ensuring trustworthy AI predictions with explicit uncertainty quantification.
method Proposes a Bayesian framework for generative ML classification models that accounts for input measurement uncertainty.
result The BQDA model outperforms other models in terms of interpretability, explicit uncertainty modeling, and computational efficiency.
New CNN approach reduces overconfidence in object classification predictions.
problem Overconfident predictions from deep models, especially SoftMax layer.
method Introduces CNN probabilistic approach using Logit layer for Bayesian inference.
result Proposed approach shows promising performance compared to SoftMax.
IA-BMA adapts model weights to inputs for better predictions.
problem Predicting with multiple models in heterogeneous settings.
method Input adaptive Bayesian Model Averaging (IA-BMA) with an input adaptive prior and amortized variational inference.
result IA-BMA consistently delivers more accurate and better-calibrated predictions.
Bayesian approach selects subsets of variables for interpretable prediction and identifies key factors in educational outcomes.
problem Challenges in subset selection for stability, regularization, and inference.
method Bayesian perspective on subset selection, deriving optimal subsets and variable importance metrics.
result Better prediction, interval estimation, and variable selection compared to competing methods.
Bayesian deep learning for graphs improves graph classification and prediction tasks.
problem Graph classification reproducibility issues and lack of uncertainty quantification.
method Developed a Bayesian Deep Learning framework for graph learning, considering discrete and continuous edge features.
result Produces unsupervised embeddings for graph classification tasks reaching state-of-the-art performance.
Bayesian classifiers converge under certain exchangeability conditions with more data.
problem Predictive accuracy of marginal classifiers under varying data conditions.
method Introduced and compared mBpc and sBpc, provided computational scheme for PE sequences, and developed parameter estimation and testing methods.
result mBpc and sBpc converge under PE conditions with increasing data, validating mBpc's use.
Bayesian deep learning uses function-space priors to improve model uncertainty and robustness.
problem Bayesian deep learning struggles with model-specific weight-space priors that are hard to interpret and specify.
method Apply a Dirichlet prior in predictive space and perform approximate function-space variational inference.
result The approach improves uncertainty quantification, scalability, and adversarial robustness in large-scale image classification.
Revises Bayesian model averaging for foundation models.
problem Ensemble pre-trained and lightly-finetuned foundation models for improved classification performance.
method Introduces trainable linear classifiers and computationally cheaper model averaging scheme (OMA).
result Ensembled models can better predict on various datasets.
New method calibrates Gaussian product experts for better predictions.
problem Erratic predictions and uncalibrated uncertainty in Gaussian product experts.
method Calibration via tempered softmax and Wasserstein barycenter for predictions.
result Improved predictions with better mean and uncertainty quantification.
It is becoming increasingly important for machine learning methods to make predictions that are interpretable as well as accurate. In many practical applications, it is of interest which features and feature interactions are relevant to the prediction task. We present a novel method, Selective Bayesian Forest Classifie…
DAMVI algorithm improves imbalanced binary classification by adjusting weights of examples and classifiers.
problem Imbalanced binary classification tasks where minority class is underrepresented.
method DAMVI algorithm increases positive example weights and optimizes classifier weights using PAC-Bayesian C-Bound.
result DAMVI outperforms state-of-the-art models on various imbalanced datasets.
Bayesian inference was once a gold standard for learning with neural networks, providing accurate full predictive distributions and well calibrated uncertainty. However, scaling Bayesian inference techniques to deep neural networks is challenging due to the high dimensionality of the parameter space. In this paper, we …
We propose a novel "tree-averaging" model that utilizes the ensemble of classification and regression trees (CART). Each constituent tree is estimated with a subset of similar data. We treat this grouping of subsets as Bayesian ensemble trees (BET) and model them as an infinite mixture Dirichlet process. We show that B…
VIB balances empirical and Bayesian approaches in predictive models.
problem Balancing empirical and Bayesian methods in predictive models.
method VIB as a compromise between empirical and Bayesian objectives.
result VIB minimizes risks due to finite sampling of targets.
Bayesian approach improves crowdsourcing predictions.
problem Estimating continuous labels from crowdsource workers.
method Variational Bayesian technique for worker noise models.
result Bayesian approaches significantly outperform non-Bayesian methods.
Improved Bayesian FL method calibrates predictions for federated learning.
problem Overconfident predictions in Bayesian FL methods for federated learning.
method β-Predictive Bayes algorithm interpolates between mixture and product of local predictive posteriors, tuning parameter β for better calibration.
result Demonstrated superior calibration compared to other baselines, even with increased data heterogeneity.
New method for uncertainty analysis in TabPFN, a state-of-the-art tabular transformer.
problem No method for uncertainty decomposition in TabPFN.
method Casted as a Bayesian predictive inference problem, derived variance estimators using predictive CLT.
result Fast to compute credible bands that target epistemic uncertainty and achieve near-nominal frequentist coverage.
Bayesian explanations are more resilient to adversarial attacks than deterministic ones.
problem Stability of saliency-based explanations under adversarial attacks in Neural Networks.
method Empirical and theoretical analysis of Bayesian vs deterministic Neural Networks.
result Bayesian explanations are more stable under adversarial perturbations and direct attacks.
Novel Bayesian model improves EEG-based BCI character selection.
problem Accurately identifying target-related responses in EEG-based BCIs.
method Probit-link Split-and-merge Gaussian Process (P-SMGP) prior for feature selection.
result Reduces computational complexity and provides interpretable statistical interpretations.
BNN-DP improves robustness analysis of Bayesian Neural Networks.
problem Ensuring robustness of Bayesian Neural Networks against adversarial attacks.
method Dynamic Programming applied to Bayesian Neural Networks as stochastic dynamical systems.
result BNN-DP provides tighter and more efficient bounds on prediction ranges compared to existing methods.
Estimating predictive uncertainty is crucial for many computer vision tasks, from image classification to autonomous driving systems. Hamiltonian Monte Carlo (HMC) is an sampling method for performing Bayesian inference. On the other hand, Dropout regularization has been proposed as an approximate model averaging techn…
Bayesian neural networks outperform calibrated neural networks for tabular data.
problem Uncertainty in neural network predictions for tabular data.
method Bayesian neural networks vs. post-hoc calibration methods.
result Bayesian neural networks yield competitive performance compared to calibrated neural networks.
Bayesian deep ensembles improve prediction accuracy in various settings.
problem Improving prediction accuracy of deep ensembles in out-of-distribution settings.
method Introducing a randomised, untrainable function to each ensemble member, enabling a posterior predictive distribution interpretation.
result Bayesian deep ensembles make more conservative predictions and outperform standard ensembles in various tasks.
This paper improves Bayesian inference for predictive models with limited data.
problem Effective uncertainty quantification for training predictive models with limited data.
method Entropy-regularized gradient estimators to approximate the Bayesian posterior.
result The method generates diverse samples from the posterior distribution efficiently.
The Bayesian framework is a well-studied and successful framework for inductive reasoning, which includes hypothesis testing and confirmation, parameter estimation, sequence prediction, classification, and regression. But standard statistical guidelines for choosing the model class and prior are not always available or…
Bayesian GBMs improve predictive uncertainty calibration for tabular data.
problem Lack of well-calibrated predictive uncertainties in gradient boosting machines.
method Variational inference with soft decision trees.
result Variational soft GBMs provide useful uncertainty estimates and maintain good predictive performance.
Deep neural networks (NNs) are powerful black box predictors that have recently achieved impressive performance on a wide spectrum of tasks. Quantifying predictive uncertainty in NNs is a challenging and yet unsolved problem. Bayesian NNs, which learn a distribution over weights, are currently the state-of-the-art for …
Transformers can approximate Bayesian inference efficiently.
problem Bayesian methods struggle with deep learning due to prior knowledge and uncertainty capture.
method Prior-Data Fitted Networks (PFNs) approximate posteriors using in-context learning.
result PFNs achieve near-perfect mimicry of Gaussian processes and significant speedups.
Bayesian graph learning improves graph representation accuracy.
problem Inaccurate graph construction from noisy data.
method Non-parametric Bayesian graph model for posterior inference of graph adjacency matrices.
result Model scales well to large graphs and improves node classification, link prediction, and recommendation tasks.
This paper improves Bayesian neural nets by using local linearization.
problem Underfitting in Bayesian neural networks.
method Local linearization of Bayesian neural networks to create a generalized linear model (GLM) for predictions.
result The GLM predictive resolves common underfitting problems of the Laplace approximation.
Bayesian neural networks improve uncertainty calibration with DAP priors.
problem Improving predictive uncertainty in deep learning models outside training data.
method Distance-Aware Prior (DAP) calibration method to correct overconfidence.
result Demonstrated effectiveness in various classification and regression tasks.
Develops variational Bayesian neural network for complex biomedical applications.
problem High computational cost of Markov Chain Monte Carlo in BNN.
method Variational Bayes inference for posterior consistency and classification accuracy.
result Developed statistical theory for posterior consistency and prediction accuracy.
In this paper we present a new Bayesian network model for classification that combines the naive-Bayes (NB) classifier and the finite-mixture (FM) classifier. The resulting classifier aims at relaxing the strong assumptions on which the two component models are based, in an attempt to improve on their classification pe…
Improved sampling for Bayesian neural networks reduces vanishing acceptance rates and increases predictive accuracy.
problem Sampling inefficiency in Bayesian neural networks, especially with deep architectures and large datasets.
method Approximate blocked Gibbs sampling to partition and sample subgroups of parameters.
result Increased predictive accuracy and quantification of predictive uncertainty in classification tasks.