Paper revisits weighted likelihood bootstrap and extends it to loss-likelihood bootstrap.
problem Generating samples from approximate Bayesian posterior of a parametric model.
method Bayesian nonparametric model with minimising expected negative log-likelihood.
result Loss-likelihood bootstrap method for posterior sampling.
New method for density estimation without approximating posterior distributions.
problem Challenges in non-smooth data distributions for Bayesian density estimation.
method Autoregressive likelihood decomposition and Gaussian process prior in a quasi-Bayesian framework.
result Achieves state-of-the-art results in small-data regimes.
New algorithm optimizes MCMC sampling for structural dynamic models.
problem Time-consuming retraining of neural networks in MCMC methods.
method Adaptive meta-learning SGHMC algorithm that optimizes sampling strategy.
result Trained sampler can be applied to various problems without retraining.
Proposes a new method for nonlinear Bayesian updates using ensemble kernel regression.
problem Nonlinear and non-Gaussian Bayesian updates for complex systems.
method Combines Kalman filtering for observed components and kernel density estimation for unobserved components, with subsampling and clustering.
result Reduces estimation errors in highly nonlinear scenarios compared to standard linear updates.
Bayesian approach for learning from synthetic data, improving model accuracy.
problem Lack of statistical properties and robust methods for learning from synthetic data.
method Bayesian paradigm to update model parameters considering synthetic data generating process and learning task.
result Novel approach outperforms standard methods in supervised learning and inference problems.
Bayesian model updates data streams with hierarchical priors.
problem Continuous model updating and adapt to changes in data distribution.
method Non-conjugate hierarchical priors and variational inference.
result Validated on real data sets, demonstrating adaptability.
Bayesian model updating uses VAEs to approximate likelihood with small data.
problem Approximating likelihood for small data sets in structural analysis.
method Uses multimodal VAEs to approximate likelihood, suitable for high-dimensional correlated observations.
result Demonstrates computational efficiency and accuracy compared to original VAE approach.
Paper proves Jeffrey's update rule minimizes relative entropy.
problem Improving Bayesian learning algorithms.
method More concise proof of Jeffrey's update rule.
result Jeffrey's update rule reduces relative entropy.
New method samples triplets from data distributions for training Triplet networks.
problem Training robust Triplet networks with discriminative triplets.
method Bayesian updating of multivariate normal distributions for dynamic class embedding sampling.
result Experimental validation on MNIST and histopathology CRC datasets shows effectiveness of the proposed method.
New learning rule simplifies Bayesian updates for deep learning.
problem Bayesian learning rule's complexity and manifold constraints.
method Lie-group approach to simplify Bayesian updates.
result New algorithm learns sparse features in deep learning.
The paper updates Bayesian CMA-ES with normal Wishart and proves lower expected covariance.
problem Improving the Bayesian CMA-ES algorithm with normal Wishart prior.
method Revisits Bayesian CMA-ES, proves lower expected covariance in normal Wishart, and presents a generalized model.
result Proves that the expected covariance is lower in the normal Wishart prior model due to convexity of the inverse.
QBVI uses natural gradients for efficient Bayesian learning.
problem Efficient Bayesian learning in complex models.
method Natural gradient updates in a black-box framework for exponential-family distributions.
result QBVI framework is effective for a wide range of Bayesian inference problems.
We study how to communicate findings of Bayesian inference to third parties, while preserving the strong guarantee of differential privacy. Our main contributions are four different algorithms for private Bayesian inference on proba-bilistic graphical models. These include two mechanisms for adding noise to the Bayesia…
Developed a Particle-Gibbs sampler for Bayesian feature allocation models.
problem Intractable exact inference in Bayesian feature allocation models.
method Particle-Gibbs sampler for feature allocation matrix updates.
result PG sampler improves performance of feature allocation models.
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.
Bayesian inference improved with classifier-based misspecification detection and tempering.
problem Model misspecification in Bayesian inference leads to overly concentrated posteriors.
method Probabilistic classifiers trained on simulated vs. observed data to estimate model misspecification and tempering level.
result Estimation of negative KL divergence provides useful diagnostic and update method.
Gradient-EM Bayesian meta-learning accelerates adaptation with reduced computation and improved robustness.
problem Efficient and robust adaptation to new tasks with uncertainty assessment.
method Extends Bayesian meta-learning with gradient-EM algorithm, decoupling inner-update from meta-update.
result Improves accuracy with less computation cost and enhanced robustness to uncertainty.
Speed up Bayesian inference for large datasets using subsampling.
problem Bayesian inference in large data problems.
method Data subsampling to speed up Sequential Monte Carlo (SMC) for static Bayesian models.
result Efficiently estimates four generalized linear models and a generalized additive model with large datasets.
BRPC online Bayesian calibration handles gradual and abrupt system changes.
problem Aligning model outputs with field observations in evolving systems.
method Bayesian Recursive Projected Calibration (BRPC) for streaming data under simulator mismatch and nonstationarity.
result Improves calibration accuracy under gradual changes and robustness under abrupt regime shifts.
Standard acquisition functions are sufficient for asynchronous Bayesian optimization.
problem Redundant and repeated queries in asynchronous Bayesian optimization.
method Conceptual analysis and theoretical guarantees of standard acquisitions.
result Standard acquisition functions achieve theoretical guarantees equivalent to Thompson sampling in asynchronous settings.
The paper examines how updates to probabilistic models influence behavior based on evidence.
problem Understanding how updates to probabilistic models influence behavior based on evidence.
method Study of KL-regularized soft updates as Bayesian posterior updates within a single probabilistic model.
result Posterior updates determine relative incentives but not absolute rewards, which are ambiguous up to context-specific baselines.
Bayesian framework reduces online optimization regret.
problem Sequential optimization in dynamic environments with bounded losses.
method PAC-Bayes theory and Bayesian updating principles.
result Achieves O ( T ) \mathcal{O}(\sqrt{T}) O ( T ) regret for bounded losses. Bayesian approach helps update deep models without forgetting past data.
problem Updating deep models sequentially without forgetting past data.
method Bayesian inference for continual learning.
result Bayesian approach enables updating model beliefs with new data.
VIPs use IPs for efficient inference in flexible models.
problem Efficient inference in flexible models like Bayesian neural networks and Gaussian processes.
method Variational Implicit Processes (VIPs) using generalised wake-sleep updates.
result VIPs provide better uncertainty estimates and lower errors compared to existing methods.
Bayesian approach updates pretrained convnet for new image categories.
problem Learning new categories with limited data.
method Bayesian procedure using pretrained convnet weights as prior.
result Competitive performance with state-of-the-art methods.
Bayesian method improves Federated Learning robustness against corrupted updates.
problem Adversarial attacks on Federated Learning models with unknown number of compromised clients.
method Adaptive Bayesian aggregation based on likelihood of clients being honest.
result Consistently achieves state-of-the-art performance across various attack types.
The study provides statistical guarantees for Bayesian variational boosting.
problem Statistical and convergence issues in variational boosting.
method Proposed a novel variational family and a functional Frank-Wolfe optimization algorithm.
result Demonstrated stochastic boundedness and provided convergence rate for boosting iterates.
Unified framework for arbitrary conditional inference using AI and Bayesian methods.
problem Limited flexibility in existing conditional inference methods.
method Bayesian generative modeling with stochastic iterative algorithm.
result Single learned model for universal conditional prediction with uncertainty quantification.
Bayesian filtering optimizes portfolio weights over time with uncertain parameters.
problem Optimizing portfolios over long periods with unknown parameters.
method Bayesian filtering through dynamic linear models for dynamic parameter estimation.
result Bayesian updating improves portfolio performance and is practical.
The paper proposes a new method for updating neural network parameters using gradient conjugate priors.
problem Learning probability distributions of observed data using neural networks.
method Gradient conjugate prior (GCP) update for neural networks, connecting to log-likelihood maximization.
result The method differs from classical Bayesian updates, leading to different limiting behavior of prior parameters.
The article introduces inferential moments for analyzing uncertain multivariable systems.
problem Handling inference tasks in uncertain multivariable systems.
method Bayesian inference and quantification of inferential moments.
result Quantification of inferential moments and their connection to mutual information.
Study how learning impacts decision-making in project expansion.
problem Optimal timing of expansion decisions based on uncertain project profitability.
method Bayesian updating model with two irreversible alternatives (exit or expansion).
result Time-to-decision is not monotonic with information arrival rate.
Bayesian principles improve agentic AI decision-making.
problem Decision-making under uncertainty in agentic AI systems.
method Bayesian decision theory applied to the orchestration layer of agentic AI.
result Bayesian principles enhance agentic AI's ability to make decisions under uncertainty.
Bayesian approach generalizes ADMM for federated learning.
problem Improving federated learning efficiency and accuracy.
method Integrates Bayesian duality with ADMM for optimization.
result New extensions of ADMM for various distributions.
Bayesian neural networks learn weights with closed-form updates.
problem Efficiently learning Bayesian neural networks with closed-form updates.
method Closed-form Bayesian inference for online learning of Gaussian-weighted BNNs.
result Closed-form expressions for sequential/online training of BNNs.
LF-IBIS learns optimal policies online without explicit likelihood.
problem Bayesian RL challenges due to intractable likelihood functions.
method Combines ABC with IBIS for online belief updates.
result Approximates posterior distributions for policies and parameters.
The hybrid Monte Carlo (HMC) algorithm is used for Bayesian analysis of the generalized autoregressive conditional heteroscedasticity (GARCH) model. The HMC algorithm is one of Markov chain Monte Carlo (MCMC) algorithms and it updates all parameters at once. We demonstrate that how the HMC reproduces the GARCH paramete…
New PAC-Bayes method updates priors without losing confidence information.
problem Lack of sequential prior updates in PAC-Bayes without losing confidence information.
method Recursive PAC-Bayes decomposition of expected loss.
result Sequential prior updates with no information loss.
New decision-theoretic characterization separates belief and decision posteriors.
problem Understanding the conditions under which loss-based updating coincides with Bayesian updating.
method Decision-theoretic approach to distinguish belief and decision posteriors.
result Generalized Bayes coincides with ordinary Bayesian updating only if the loss is proportional to negative log-likelihood.
We investigate different strategies for active learning with Bayesian deep neural networks. We focus our analysis on scenarios where new, unlabeled data is obtained episodically, such as commonly encountered in mobile robotics applications. An evaluation of different strategies for acquisition, updating, and final trai…
VCoTTA uses variational Bayesian methods to adapt models under continuous domain shifts.
problem Error accumulation in continual test-time adaptation.
method VCoTTA employs variational Bayesian techniques to update a Bayesian Neural Network (BNN) during testing, combining priors from source and teacher models.
result VCoTTA effectively mitigates error accumulation in CTTA, as shown by experimental results on three datasets.
Bayesian approach improves CMA-ES algorithm for faster convergence.
problem Improving the CMA-ES algorithm for faster convergence.
method Deriving optimal updates for CMA-ES parameters using conjugate priors.
result New versions of CMA-ES converge faster with normal-Wishart or normal-Inverse Wishart priors.
LatentTrack generates model parameters online for nonstationary data.
problem Online probabilistic prediction under nonstationary dynamics.
method Sequential neural architecture with latent filtering and amortized inference.
result Consistently lower negative log-likelihood and mean squared error than baselines.
Bayesian meta-learning method learns uncertainty from few data.
problem Learning uncertainty from limited data in meta-learning.
method Combines gradient-based meta-learning with nonparametric variational inference.
result Robust meta-update mechanism prevents overfitting.
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.
Bayesian inference for expensive likelihoods using Langevin Monte Carlo with NF.
problem Sampling from complex posterior distributions with expensive likelihoods.
method Deterministic Langevin equation with NF gradient, Metropolis-Hastings updates.
result Competitive performance compared to state-of-the-art methods.
CL-BRUNO model tackles continual learning with scalable Bayesian updates.
problem Deploying CL methods to real-world decision-making problems due to computational cost and lack of uncertainty quantification.
method Probabilistic, Neural Process-based CL model using deep-generative models for scalable and tractable Bayesian update and prediction.
result CL-BRUNO outperforms existing methods on natural image and biomedical data sets.
NeuralSurv models survival analysis with Bayesian uncertainty.
problem Capturing time-varying risk relationships in survival analysis.
method Two-stage data-augmentation scheme, mean-field variational algorithm, coordinate-ascent updates, locally linearized Bayesian neural network.
result Delivers superior calibration compared to state-of-the-art models.