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.
The paper studies sparsity in EBF with hyperpriors and proposes a PALM algorithm.
Proposes a new hyperprior and predictive criterion for weakly informative hyperprior in relevance vector machine.
Paper introduces variational inference for Bayesian inverse problems with gamma hyperpriors.
Few-shot learning aims to train efficient predictive models with a few examples. The lack of training data leads to poor models that perform high-variance or low-confidence predictions. In this paper, we propose to meta-learn the ensemble of epoch-wise empirical Bayes models (E3BM) to achieve robust predictions. "Epoch…
Bayesian neural networks are shown to be minimax and admissible under certain conditions.
Recently, impressive denoising results have been achieved by Bayesian approaches which assume Gaussian models for the image patches. This improvement in performance can be attributed to the use of per-patch models. Unfortunately such an approach is particularly unstable for most inverse problems beyond denoising. In th…
Improves feature selection in high-dimensional data using LLM-generated weights.
Paper proposes fully Bayesian approach for RVM classification, improving accuracy especially in imbalanced data.
We present the first general purpose framework for marginal maximum a posteriori estimation of probabilistic program variables. By using a series of code transformations, the evidence of any probabilistic program, and therefore of any graphical model, can be optimized with respect to an arbitrary subset of its sampled …
We address the problem of unsupervised disentanglement of latent representations learnt via deep generative models. In contrast to current approaches that operate on the evidence lower bound (ELBO), we argue that statistical independence in the latent space of VAEs can be enforced in a principled hierarchical Bayesian …
We propose a method to classify the causal relationship between two discrete variables given only the joint distribution of the variables, acknowledging that the method is subject to an inherent baseline error. We assume that the causal system is acyclicity, but we do allow for hidden common causes. Our algorithm presu…
The problem of low rank matrix completion is considered in this paper. To exploit the underlying low-rank structure of the data matrix, we propose a hierarchical Gaussian prior model, where columns of the low-rank matrix are assumed to follow a Gaussian distribution with zero mean and a common precision matrix, and a W…
Paper improves deep point cloud compression techniques.
Two algorithms improve GP bandits by selecting priors and minimizing regret.
New method designs fairer transport plans with uncertainty.
High-dimensional shrinkage risk depends on the default prior for the common scale.
New method reduces bias in sparse Bayesian learning.
Bayesian inference simplified for machine learning models.
Adaptive Bayesian model for covariate-dependent power spectra analysis.
We investigate the choice of tuning parameters for a Bayesian multi-level group lasso model developed for the joint analysis of neuroimaging and genetic data. The regression model we consider relates multivariate phenotypes consisting of brain summary measures (volumetric and cortical thickness values) to single nucleo…
Fast Bayesian inference with adaptable priors for real-time applications.
CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powerful technique for tensor completion through explicitly capturing the multilinear latent factors. The existing CP algorithms require the tensor rank to be manually specified, however, the determination of tensor rank remains a challenging problem e…
Improved neural image compression with refined latent representations.
Bayesian KANs achieve near-minimax posterior contraction rates in anisotropic Besov spaces.
A principled approach to characterize the hidden structure of networks is to formulate generative models, and then infer their parameters from data. When the desired structure is composed of modules or "communities", a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into group…