arXiv research

Proposes a new method for nonlinear Bayesian updates using ensemble kernel regression.

New method for density estimation without approximating posterior distributions.

Bayesian model updating uses VAEs to approximate likelihood with small data.

Paper proves Jeffrey's update rule minimizes relative entropy.

New method samples triplets from data distributions for training Triplet networks.

New algorithm optimizes MCMC sampling for structural dynamic models.

Bayesian uncertainty quantification is flawed, according to new research.

Gradient-EM Bayesian meta-learning accelerates adaptation with reduced computation and improved robustness.

In this paper we revisit the weighted likelihood bootstrap, a method that generates samples from an approximate Bayesian posterior of a parametric model. We show that the same method can be derived, without approximation, under a Bayesian nonparametric model with the parameter of interest defined as minimising an expec…

New learning rule simplifies Bayesian updates for deep learning.

Bayesian approach for learning from synthetic data, improving model accuracy.

QBVI uses natural gradients for efficient Bayesian learning.

The paper examines how updates to probabilistic models influence behavior based on evidence.

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…

Bayesian approach helps update deep models without forgetting past data.

Bayesian method improves Federated Learning robustness against corrupted updates.

The paper deals with learning probability distributions of observed data by artificial neural networks. We suggest a so-called gradient conjugate prior (GCP) update appropriate for neural networks, which is a modification of the classical Bayesian update for conjugate priors. We establish a connection between the gradi…

Bayesian neural networks learn weights with closed-form updates.

LF-IBIS learns optimal policies online without explicit 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.

NeuralSurv models survival analysis with Bayesian uncertainty.

Bayesian Federated Learning improves model reliability in dynamic environments.

A new Bayesian filtering method speeds up stochastic Newton optimization.

A novel method for learning Bayesian network structures from decentralized data, balancing privacy and efficiency.

Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model uncertainty inherent in the problem. In this paper, we propose a novel Bayesian model-agnostic meta-learning method. The proposed method combines scalable gradient-based meta-learning with non…

Kernel interpolation speeds up online Gaussian process updates.

BRPC online Bayesian calibration handles gradual and abrupt system changes.

For a long investment time horizon, it is preferable to rebalance the portfolio weights at intermediate times. This necessitates a multi-period market model in which portfolio optimization is usually done through dynamic programming. However, this assumes a known distribution for the parameters of the financial time se…

Standard acquisition functions are sufficient for asynchronous Bayesian optimization.

A framework for navigating environments with spatially correlated obstacles and uncertain blockage status.

Step-DAD improves BED by periodically updating a design policy during experiments.

Bayesian framework reduces online optimization regret.

Paper introduces robust Gaussian process regression without sacrificing computational efficiency.

Test-time training adapts a pretrained model to each prompt via parameter updates, improving accuracy under pretraining-to-test distribution shifts.

Bayesian feature allocation models are a popular tool for modelling data with a combinatorial latent structure. Exact inference in these models is generally intractable and so practitioners typically apply Markov Chain Monte Carlo (MCMC) methods for posterior inference. The most widely used MCMC strategies rely on an e…

The article introduces inferential moments for analyzing uncertain multivariable systems.

New PAC-Bayes method updates priors without losing confidence information.

In this paper, we propose a probabilistic optimization method, named probabilistic incremental proximal gradient (PIPG) method, by developing a probabilistic interpretation of the incremental proximal gradient algorithm. We explicitly model the update rules of the incremental proximal gradient method and develop a syst…

Bayesian principles improve agentic AI decision-making.

Proposes a probabilistic digital twin for dynamical systems using sparse Bayesian learning.

Bayesian model predicts oncology demand trends with high accuracy.

Improved neural network ensembles using Stein Variational Newton updates.

The study provides statistical guarantees for Bayesian variational boosting.

We introduce the implicit processes (IPs), a stochastic process that places implicitly defined multivariate distributions over any finite collections of random variables. IPs are therefore highly flexible implicit priors over functions, with examples including data simulators, Bayesian neural networks and non-linear tr…

Transformers mimic Bayesian reasoning in controlled settings, revealing geometric mechanisms.

New algorithms for fast online decision making using neural networks and martingale posteriors.

Bayesian framework improves variance component estimation in MET data.