arXiv research

The paper establishes conditions for Bayesian consistency in supremum metric.

This paper establishes the asymptotic consistency of the {\it loss-calibrated variational Bayes} (LCVB) method. LCVB was proposed in~\cite{LaSiGh2011} as a method for approximately computing Bayesian posteriors in a `loss aware' manner. This methodology is also highly relevant in general data-driven decision-making con…

The paper revisits and improves on a Bayesian relevance vector machine method for small sample sizes.

Proposes a method to learn sparse deep neural networks with theoretical guarantees.

The paper optimizes LLM accuracy by stopping early based on consistent answers.

Bayesian methods often misinterpret data and asymptotic concepts.

Paper improves BN structure learning from incomplete data.

Bayesian DDR models complex multivariate distributions.

Adaptive Bayesian quadrature (ABQ) is a powerful approach to numerical integration that empirically compares favorably with Monte Carlo integration on problems of medium dimensionality (where non-adaptive quadrature is not competitive). Its key ingredient is an acquisition function that changes as a function of previou…

A popular approach to semi-supervised learning proceeds by endowing the input data with a graph structure in order to extract geometric information and incorporate it into a Bayesian framework. We introduce new theory that gives appropriate scalings of graph parameters that provably lead to a well-defined limiting post…

Bayesian inference uses Stein discrepancy for robustness in intractable likelihoods.

Bayesian SSR on graphs improves regression with noisy labels.

Bayesian method improves predictions in overparameterized nonlinear regression.

New method uses entropy to generate multiple plausible causal maps.

A key challenge for modern Bayesian statistics is how to perform scalable inference of posterior distributions. To address this challenge, variational Bayes (VB) methods have emerged as a popular alternative to the classical Markov chain Monte Carlo (MCMC) methods. VB methods tend to be faster while achieving comparabl…

We propose a vector-valued regression problem whose solution is equivalent to the reproducing kernel Hilbert space (RKHS) embedding of the Bayesian posterior distribution. This equivalence provides a new understanding of kernel Bayesian inference. Moreover, the optimization problem induces a new regularization for the …

New Bayesian method for joint sparse parameter inference.

New methods for parameter estimation in mechanistic models using data-consistent inversion.

Bayesian framework improves deep classifier reliability.

CCE improves anomaly detection metrics by measuring both confidence and consistency.

Develops variational Bayesian neural network for complex biomedical applications.

Bayesian method recovers causal structure in SEMs with equal error variances.

Bayesian model infers factor dimensionality and sparse loading matrix adaptively.

sBayFDNN bridges deep learning and functional data analysis for complex, structured data.

Bayesian networks are typically faithful, with implications for causal inference.

Paper improves Bayesian regret bounds for Thompson Sampling in reinforcement learning.

We consider the problem of Bayesian learning on sensitive datasets and present two simple but somewhat surprising results that connect Bayesian learning to "differential privacy:, a cryptographic approach to protect individual-level privacy while permiting database-level utility. Specifically, we show that that under s…

Bayesian framework improves reliability and consistency of model explanations.

New algorithm optimizes Bayesian network learning from Gaussian data.

Recent techniques built on Generative Adversarial Networks (GANs), such as Cycle-Consistent GANs, are able to learn mappings among different domains built from unpaired datasets, through min-max optimization games between generators and discriminators. However, it remains challenging to stabilize the training process a…

We formalize the problem of learning interdomain correspondences in the absence of paired data as Bayesian inference in a latent variable model (LVM), where one seeks the underlying hidden representations of entities from one domain as entities from the other domain. First, we introduce implicit latent variable models,…

Bayesian networks with latent variables are characterized and their likelihoods compared.

Bayesian methods improve industrial modeling under uncertainty.

CNN-F uses generative feedback to improve neural networks' robustness to perturbations.

Paper analyzes consistency of Bayesian and machine learning methods for hierarchical parameter estimation.

Paper improves robustness of ABI with unlabeled data.

Study nonconcave portfolio choice with smooth ambiguity and Bayesian learning.

Optimizes portfolio construction using Bayesian methods and variational techniques.

Study on Dirichlet process mixtures for clustering consistency.

Develops a new Bayesian inference method for discrete data.

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…

We study the computational complexity of Markov chain Monte Carlo (MCMC) methods for high-dimensional Bayesian linear regression under sparsity constraints. We first show that a Bayesian approach can achieve variable-selection consistency under relatively mild conditions on the design matrix. We then demonstrate that t…

In this article, we investigate large sample properties of model selection procedures in a general Bayesian framework when a closed form expression of the marginal likelihood function is not available or a local asymptotic quadratic approximation of the log-likelihood function does not exist. Under appropriate identifi…

This paper addresses metaconsistency in Bayesian inference for metastable systems.

Proposes PE-GP-UCB for time-varying Bayesian optimisation.

The problem of state estimation for unobservable distribution systems is considered. A deep learning approach to Bayesian state estimation is proposed for real-time applications. The proposed technique consists of distribution learning of stochastic power injection, a Monte Carlo technique for the training of a deep ne…

Paper introduces IO-NPF for efficient Bayesian experimental design.

SOLBP extends efficient inference to uncertain Bayesian networks.