arXiv research

New method handles unknown task boundaries in continual learning.

BONG optimizes Bayesian inference online with natural gradient descent.

We present a family of expectation-maximization (EM) algorithms for binary and negative-binomial logistic regression, drawing a sharp connection with the variational-Bayes algorithm of Jaakkola and Jordan (2000). Indeed, our results allow a version of this variational-Bayes approach to be re-interpreted as a true EM al…

Trading off exploration and exploitation in an unknown environment is key to maximising expected return during learning. A Bayes-optimal policy, which does so optimally, conditions its actions not only on the environment state but on the agent's uncertainty about the environment. Computing a Bayes-optimal policy is how…

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

Streaming variational Bayes (SVB) is successful in learning LDA models in an online manner. However previous attempts toward developing online Monte-Carlo methods for LDA have little success, often by having much worse perplexity than their batch counterparts. We present a streaming Gibbs sampling (SGS) method, an onli…

Paper quantifies uncertainties in EIS spectra of SOFCs, proposing VB method for online monitoring.

Robust Kalman filtering method for outlier detection.

State-space models have been successfully used for more than fifty years in different areas of science and engineering. We present a procedure for efficient variational Bayesian learning of nonlinear state-space models based on sparse Gaussian processes. The result of learning is a tractable posterior over nonlinear dy…

Empirical Bayes rates via variational approximations and prior decomposition.

Adaptive variational Bayes framework improves inference adaptively.

VBphenoR uses variational Bayes for EHR-based patient phenotyping.

Bayesian framework reduces online optimization regret.

Simplified Variational Bayes for easier inference.

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

Unified empirical and variational Bayes for unnormalized densities.

A practical guide to Variational Bayes methods.

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 …

Proposes OBS, a method to adaptively combine Bayesian models online.

New clustering algorithm for time series data using RNN and variational Bayes.

Improved IVON boosts LoRA model accuracy and calibration.

A framework uses variational Bayes for solving inverse problems efficiently.

New online GP algorithm offers performance guarantees for streaming data.

A new sequential method estimates Poisson means in streaming data, achieving optimality and efficiency.

Develops a fast variational approximation for high-dimensional empirical Bayes posteriors.

Parameter estimation for model-based clustering using a finite mixture of normal inverse Gaussian (NIG) distributions is achieved through variational Bayes approximations. Univariate NIG mixtures and multivariate NIG mixtures are considered. The use of variational Bayes approximations here is a substantial departure fr…

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

VAR-GPs solve continual learning by updating posteriors sequentially.

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 …

The paper improves Gaussian process regression by optimizing hyperparameters.

Efficiently identifies important variables in binary outcomes using variational Bayes.

Bayesian supertrees method uses variational Bayes for overlapping taxon subsets.

Surprise-based learning allows agents to rapidly adapt to non-stationary stochastic environments characterized by sudden changes. We show that exact Bayesian inference in a hierarchical model gives rise to a surprise-modulated trade-off between forgetting old observations and integrating them with the new ones. The mod…

Quantum annealing improves VB inference, avoiding local minima.

Paper proves LCVB method's consistency in Bayesian posteriors and decision rules.

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

Estimates log marginal likelihood using multilevel Monte Carlo.

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…

VB uses natural gradients in information geometry.

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

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

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

Improved Bayesian uncertainty quantification using variational bagging.

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

RQMC improves optimization in variational Bayes problems.

In this document we are going to derive the equations needed to implement a Variational Bayes i-vector extractor. This can be used to extract longer i-vectors reducing the risk of overfittig or to adapt an i-vector extractor from a database to another with scarce development data. This work is based on Patrick Kenny's …

New technologies for recording the activity of large neural populations during complex behavior provide exciting opportunities for investigating the neural computations that underlie perception, cognition, and decision-making. Nonlinear state space models provide an interpretable signal processing framework by combinin…