arXiv research

New Bayesian optimization models for efficient material screening.

Conditional independence tests (CI tests) have received special attention lately in Machine Learning and Computational Intelligence related literature as an important indicator of the relationship among the variables used by their models. In the field of Probabilistic Graphical Models (PGM)--which includes Bayesian Net…

A new framework for private Bayesian tests maintains interpretability and computational efficiency.

nFBST tests neural networks using Bayesian methods.

Bayesian methods improve group testing for identifying infected patients.

Proposes a semi-Bayesian nonparametric estimator for MMD in GOF tests and GANs.

Bayesian MAML outperforms MAML in meta learning tasks with theoretical guarantees.

Paper proposes a new method for more accurate group testing of infected patients.

Unified Bayesian framework improves clinical trial hypothesis testing.

GTBO uses group testing to optimize high-dimensional functions efficiently.

We consider testing and learning problems on causal Bayesian networks as defined by Pearl (Pearl, 2009). Given a causal Bayesian network $\mathcal{M}$ on a graph with $n$ discrete variables and bounded in-degree and bounded `confounded components', we show that $O(\log n)$ interventions on an unknown causal Bayesian ne…

This letter presents a novel Block Bayesian Hypothesis Testing Algorithm (Block-BHTA) for reconstructing block sparse signals with unknown block structures. The Block-BHTA comprises the detection and recovery of the supports, and the estimation of the amplitudes of the block sparse signal. The support detection and rec…

Bayesian method selects important covariates in modal regression.

Discussing hybrid models in Bayesian networks.

New method explains sensitivity of test data uncertainty in Bayesian inference.

Proposes a new method to improve Bayesian computation accuracy using flexible classification.

BYOV combines SSL and Bayesian methods for uncertainty estimation.

VCoTTA uses variational Bayesian methods to adapt models under continuous domain shifts.

In literature there are several studies on the performance of Bayesian network structure learning algorithms. The focus of these studies is almost always the heuristics the learning algorithms are based on, i.e. the maximisation algorithms (in score-based algorithms) or the techniques for learning the dependencies of e…

A test for sparsity in Bayesian networks helps choose algorithms.

Bayesian test assesses dependence between mixed data types.

Optimizes group testing for COVID-19 to reduce test numbers.

Bayesian method improves adaptive testing item selection, ensuring full item exposure.

GTBO uses group testing to optimize high-dimensional functions efficiently.

VarFA efficiently estimates student skill levels with uncertainty for adaptive testing.

Usually one compares the accuracy of two competing classifiers via null hypothesis significance tests (nhst). Yet the nhst tests suffer from important shortcomings, which can be overcome by switching to Bayesian hypothesis testing. We propose a Bayesian hierarchical model which jointly analyzes the cross-validation res…

Bayesian analysis reveals asymmetry in financial data.

Transformer pretraining yields strong EB performance without explicit adaptation.

Formula derived for sample complexity in binary hypothesis testing.

Variational Prediction simplifies Bayesian inference without test time costs.

New test identifies specific biological parameters for personalized CVD detection.

A new method for assessing Bayesian sampling quality, PSD, is proposed and shown to be more powerful and efficient.

Empirical analysis serves as an important complement to theoretical analysis for studying practical Bayesian optimization. Often empirical insights expose strengths and weaknesses inaccessible to theoretical analysis. We define two metrics for comparing the performance of Bayesian optimization methods and propose a ran…

Bayesian methods detect significant IIA violations in similarity choice data.

This letter proposes a low-computational Bayesian algorithm for noisy sparse recovery in the context of one bit compressed sensing with sensing matrix perturbation. The proposed algorithm which is called BHT-MLE comprises a sparse support detector and an amplitude estimator. The support detector utilizes Bayesian hypot…

Bayesian method tests Granger causality in functional time series.

The PC algorithm is a popular method for learning the structure of Gaussian Bayesian networks. It carries out statistical tests to determine absent edges in the network. It is hence governed by two parameters: (i) The type of test, and (ii) its significance level. These parameters are usually set to values recommended …

Testing whether a probability distribution is compatible with a given Bayesian network is a fundamental task in the field of causal inference, where Bayesian networks model causal relations. Here we consider the class of causal structures where all correlations between observed quantities are solely due to the influenc…

Paper proposes a method to solve sparse Bayesian learning problems efficiently.

Develops a method for stress testing correlations of financial portfolios.

The paper solves IRL for Bayesian stopping time problems.

Robust hypothesis testing designs a test for worst-case distributions using kernel methods.

New insights on Bayesian models show stochasticity doesn't always improve accuracy.

Paper proposes efficient method for evaluating Bayesian models in imaging.

One of the goals of probabilistic inference is to decide whether an empirically observed distribution is compatible with a candidate Bayesian network. However, Bayesian networks with hidden variables give rise to highly non-trivial constraints on the observed distribution. Here, we propose an information-theoretic appr…

Paper proposes a fast method to detect changes in audiometric function.

Bayesian method optimizes interventions for causal discovery.

New framework improves model reliability under distribution shifts.