A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Deep learning improves Bayes factor computation for likelihood-free models.
The paper derives a formula for factorizing categorical data to improve Bayes classifiers.
Bayes factors and relative belief ratios are compared as measures of statistical evidence.
Naive Bayes estimator is widely used in text classification problems. However, it doesn't perform well with small-size training dataset. We propose a new method based on Naive Bayes estimator to solve this problem. A correlation factor is introduced to incorporate the correlation among different classes. Experimental r…
Bayesian inference improves neural network pruning efficiency.
New method for hyperparameter tuning in sparse matrix factorization.
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…
We presented Bayesian portfolio selection strategy, via the factor asset pricing model. If the market is information efficient, the proposed strategy will mimic the market; otherwise, the strategy will outperform the market. The strategy depends on the selection of a portfolio via Bayesian multiple testing methodol…
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 …
A framework uses variational Bayes for solving inverse problems efficiently.
We study the Nonparametric Maximum Likelihood Estimator (NPMLE) for estimating Gaussian location mixture densities in -dimensions from independent observations. Unlike usual likelihood-based methods for fitting mixtures, NPMLEs are based on convex optimization. We prove finite sample results on the Hellinger accurac…
Researchers estimate optimal PAC-Bayes bounds using Hamiltonian Monte Carlo.
A new framework for private Bayesian tests maintains interpretability and computational efficiency.
Unified Bayesian framework improves clinical trial hypothesis testing.
Evidence Networks simplify Bayesian model comparison for complex models.
We propose a family of novel hierarchical Bayesian deep auto-encoder models capable of identifying disentangled factors of variability in data. While many recent attempts at factor disentanglement have focused on sophisticated learning objectives within the VAE framework, their choice of a standard normal as the latent…
Proposes CSG model to separate semantic and variation factors for OOD prediction.
Adaptive variational Bayes framework improves inference adaptively.
Bayesian method tests Granger causality in functional time series.
Recent studies have shown that imbalance ratio is not the only cause of the performance loss of a classifier in imbalanced data classification. In fact, other data factors, such as small disjuncts, noises and overlapping, also play the roles in tandem with imbalance ratio, which makes the problem difficult. Thus far, t…
New method improves Bayesian inference for parametric models, robust to misspecification.
Lower bounds on Bayes risk for realizable models derived using information theory.
New decision-theoretic characterization separates belief and decision posteriors.
Adaptive framework improves NB accuracy by fusing two index categories.
Efficiently identifies important variables in binary outcomes using variational Bayes.
Paper proposes C-STM for multimodal neuroimaging data classification.
Variational Bayes (VB) is a recent approximate method for Bayesian inference. It has the merit of being a fast and scalable alternative to Markov Chain Monte Carlo (MCMC) but its approximation error is often unknown. In this paper, we derive the approximation error of VB in terms of mean, mode, variance, predictive den…
IIC provides a PAC-Bayes bound for interpolating models, revealing factors affecting generalization.
Deep neural networks are optimal for dependent data using PAC-Bayes bounds.
Oracle inequality for sparse neural nets adapts to unknown structure.
We analyse the matrix factorization problem. Given a noisy measurement of a product of two matrices, the problem is to estimate back the original matrices. It arises in many applications such as dictionary learning, blind matrix calibration, sparse principal component analysis, blind source separation, low rank matrix …
We introduce negative binomial matrix factorization (NBMF), a matrix factorization technique specially designed for analyzing over-dispersed count data. It can be viewed as an extension of Poisson matrix factorization (PF) perturbed by a multiplicative term which models exposure. This term brings a degree of freedom fo…
Bayesian method optimizes interventions for causal discovery.
Improved IFA with Generative Adversarial Networks for high-dimensional latent variables.
Thermodynamic integration (TI) for computing marginal likelihoods is based on an inverse annealing path from the prior to the posterior distribution. In many cases, the resulting estimator suffers from high variability, which particularly stems from the prior regime. When comparing complex models with differences in a …
The digital telecommunications receiver is an important context for inference methodology, the key objective being to minimize the expected loss function in recovering the transmitted information. For that criterion, the optimal decision is the Bayesian minimum-risk estimator. However, the computational load of the Bay…
New estimator reduces risk in slate bandits by leveraging Bayes risk criterion.
LPF provides formal guarantees for aggregating multi-evidence in probabilistic tasks.
Efficient Bayesian LMM framework for high-dimensional longitudinal data.
New framework shows cross-attention improves multi-modal in-context learning.
Neural Network based controllers hold enormous potential to learn complex, high-dimensional functions. However, they are prone to overfitting and unwarranted extrapolations. PAC Bayes is a generalized framework which is more resistant to overfitting and that yields performance bounds that hold with arbitrarily high pro…
In this paper we review the concepts of Bayesian evidence and Bayes factors, also known as log odds ratios, and their application to model selection. The theory is presented along with a discussion of analytic, approximate and numerical techniques. Specific attention is paid to the Laplace approximation, variational Ba…
We consider the problem of discriminative factor analysis for data that are in general non-Gaussian. A Bayesian model based on the ranks of the data is proposed. We first introduce a new {\em max-margin} version of the rank-likelihood. A discriminative factor model is then developed, integrating the max-margin rank-lik…
The CAP slope is Bayes' theorem in cumulative coordinates, unlocking the weight of evidence, Somers' D, and Gini coefficient.
Advocates for a new posterior that predicts better than classical and generalised Bayes.
We study methods for simultaneous analysis of many noisy experiments in the presence of rich covariate information. The goal of the analyst is to optimally estimate the true effect underlying each experiment. Both the noisy experimental results and the auxiliary covariates are useful for this purpose, but neither data …
New method selects FMM components via variational Bayes.
The purpose of this paper is to propose a time-varying vector autoregressive model (TV-VAR) for forecasting multivariate time series. The model is casted into a state-space form that allows flexible description and analysis. The volatility covariance matrix of the time series is modelled via inverted Wishart and singul…