arXiv research

A new method combines Laplace and Variational Bayes for scalable inference.

Neural Empirical Bayes estimates source distributions from noisy simulations.

Empirical Bayes rates via variational approximations and prior decomposition.

Simplified Variational Bayes for easier inference.

Adaptive variational Bayes framework improves inference adaptively.

This paper presents studies on a deterministic annealing algorithm based on quantum annealing for variational Bayes (QAVB) inference, which can be seen as an extension of the simulated annealing for variational Bayes (SAVB) inference. QAVB is as easy as SAVB to implement. Experiments revealed QAVB finds a better local …

Bayesian Empirical Bayes extends EB to complex structures using probabilistic symmetry.

A practical guide to Variational Bayes methods.

The cold posterior effect is explored through PAC-Bayes bounds for small sample sizes.

Efficient neural Bayes estimators for censored peaks-over-threshold models improve inference speed and accuracy.

We propose a novel interpretation of the collapsed variational Bayes inference with a zero-order Taylor expansion approximation, called CVB0 inference, for latent Dirichlet allocation (LDA). We clarify the properties of the CVB0 inference by using the alpha-divergence. We show that the CVB0 inference is composed of two…

ContraBAR uses contrastive learning to learn Bayes-optimal policies in RL.

A scalable method for accurate inference of low-dimensional parameters in high-dimensional linear regression.

Variational Bayes (VB) inference is one of the most important algorithms in machine learning and widely used in engineering and industry. However, VB is known to suffer from the problem of local optima. In this Letter, we generalize VB by using quantum mechanics, and propose a new algorithm, which we call quantum annea…

Bayesian inference improves neural network pruning efficiency.

This paper introduces Bayes Hilbert spaces for efficient posterior approximation.

Quantum annealing improves VB inference, avoiding local minima.

A new method improves likelihood-free Bayesian inference by transforming summary statistics and using efficient Variational Bayes.

Kernel Bayes' rule has been proposed as a nonparametric kernel-based method to realize Bayesian inference in reproducing kernel Hilbert spaces. However, we demonstrate both theoretically and experimentally that the prediction result by kernel Bayes' rule is in some cases unnatural. We consider that this phenomenon is i…

We introduce Deep Variational Bayes Filters (DVBF), a new method for unsupervised learning and identification of latent Markovian state space models. Leveraging recent advances in Stochastic Gradient Variational Bayes, DVBF can overcome intractable inference distributions via variational inference. Thus, it can handle …

Neural Bayes methods simplify fitting complex bivariate extremal models.

Improved model-based estimation through tempered Bayes filter.

New method improves Bayesian inference for parametric models, robust to misspecification.

Bayes-CATSI uses variational Bayesian deep learning for medical time series data imputation.

Attention-only transformers learn from context via two stages of inference.

Stochastic variational inference for Bayesian deep neural network (DNN) requires specifying priors and approximate posterior distributions over neural network weights. Specifying meaningful weight priors is a challenging problem, particularly for scaling variational inference to deeper architectures involving high dime…

A new optimization algorithm for Gaussian Variational Inference on precision matrices.

New method for high-dimensional linear regression using empirical Bayes.

A framework uses variational Bayes for solving inverse problems efficiently.

There are three principle paradigms of statistical inference: (i) Bayesian, (ii) information-based and (iii) frequentist inference. We describe an objective prior (the weighting or $w$-prior) which unifies objective Bayes and information-based inference. The $w$-prior is chosen to make the marginal probability an unbia…

Transformer pretraining yields strong EB performance without explicit adaptation.

Early-stopped aggregation improves computational efficiency in adaptive statistical inference.

SMI uses mixture models to improve SVGD's performance in Bayesian inference.

This paper improves meta-learning by developing new PAC-Bayes bounds.

VBphenoR uses variational Bayes for EHR-based patient phenotyping.

Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal with uncertainty when learning from finite data. Among approaches to realize probabilistic inference in deep neural networks, variational Bayes (VB) is theoretically grounded, generally applicable, and computationally effic…

We rebias estimates to improve interval calibration and prediction accuracy.

Fast variational Bayes methods improve geospatial data analysis speed and accuracy.

Bayesian predictive inference analyzes a dataset to make predictions about new observations. When a model does not match the data, predictive accuracy suffers. We develop population empirical Bayes (POP-EB), a hierarchical framework that explicitly models the empirical population distribution as part of Bayesian analys…

Deep neural networks have achieved impressive results on a wide variety of tasks. However, quantifying uncertainty in the network's output is a challenging task. Bayesian models offer a mathematical framework to reason about model uncertainty. Variational methods have been used for approximating intractable integrals t…

FAB-PPI uses prior knowledge to improve prediction-powered inference.

Improved Bayesian uncertainty quantification using variational bagging.

We present a particle flow realization of Bayes' rule, where an ODE-based neural operator is used to transport particles from a prior to its posterior after a new observation. We prove that such an ODE operator exists. Its neural parameterization can be trained in a meta-learning framework, allowing this operator to re…

Bayesian supertrees method uses variational Bayes for overlapping taxon subsets.

Unified approach to Bayesian inference with guarantees on covariance matrices.

Bayesian Hierarchical Invariant Prediction refines ICP for better scalability and prior integration.

We present SDA-Bayes, a framework for (S)treaming, (D)istributed, (A)synchronous computation of a Bayesian posterior. The framework makes streaming updates to the estimated posterior according to a user-specified approximation batch primitive. We demonstrate the usefulness of our framework, with variational Bayes (VB) …

A new meta-learning method using shared variational inference.