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Tag: variational

  • Scalable Mean-Field Variational Inference via Preconditioned Primal-Dual Optimization

    Scalable Mean-Field Variational Inference via Preconditioned Primal-Dual Optimization arXiv:2602.07632v1 Announce Type: new Abstract: In this work, we investigate the large-scale mean-field variational inference (MFVI) problem from a mini-batch primal-dual perspective. By reformulating MFVI as a constrained finite-sum problem, we develop a novel primal-dual algorithm based on an augmented Lagrangian formulation, termed primal-dual variational inference (PD-VI).…

  • Variational Estimators for Node Popularity Models

    Variational Estimators for Node Popularity Models arXiv:2511.17783v1 Announce Type: new Abstract: Node popularity is recognized as a key factor in modeling real-world networks, capturing heterogeneity in connectivity across communities. This concept is equally important in bipartite networks, where nodes in different partitions may exhibit varying popularity patterns, motivating models such as the Two-Way Node Popularity…

  • Theory and computation for structured variational inference

    Theory and computation for structured variational inference arXiv:2511.09897v1 Announce Type: new Abstract: Structured variational inference constitutes a core methodology in modern statistical applications. Unlike mean-field variational inference, the approximate posterior is assumed to have interdependent structure. We consider the natural setting of star-structured variational inference, where a root variable impacts all the other ones. We…

  • VIKING: Deep variational inference with stochastic projections

    VIKING: Deep variational inference with stochastic projections arXiv:2510.23684v1 Announce Type: new Abstract: Variational mean field approximations tend to struggle with contemporary overparametrized deep neural networks. Where a Bayesian treatment is usually associated with high-quality predictions and uncertainties, the practical reality has been the opposite, with unstable training, poor predictive power, and subpar calibration. Building upon…

  • Neural variational inference for cutting feedback during uncertainty propagation

    Neural variational inference for cutting feedback during uncertainty propagation arXiv:2510.10268v1 Announce Type: new Abstract: In many scientific applications, uncertainty of estimates from an earlier (upstream) analysis needs to be propagated in subsequent (downstream) Bayesian analysis, without feedback. Cutting feedback methods, also termed cut-Bayes, achieve this by constructing a cut-posterior distribution that prevents backward information flow.…

  • A Hierarchical Variational Graph Fused Lasso for Recovering Relative Rates in Spatial Compositional Data

    A Hierarchical Variational Graph Fused Lasso for Recovering Relative Rates in Spatial Compositional Data arXiv:2509.20636v1 Announce Type: new Abstract: The analysis of spatial data from biological imaging technology, such as imaging mass spectrometry (IMS) or imaging mass cytometry (IMC), is challenging because of a competitive sampling process which convolves signals from molecules in a single…

  • Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference

    Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference arXiv:2505.21721v1 Announce Type: new Abstract: We prove that, given a mean-field location-scale variational family, black-box variational inference (BBVI) with the reparametrization gradient converges at an almost dimension-independent rate. Specifically, for strongly log-concave and log-smooth targets, the number of iterations for BBVI with a sub-Gaussian family to achieve…

  • Variational Formulation of the Particle Flow Particle Filter

    Variational Formulation of the Particle Flow Particle Filter arXiv:2505.04007v1 Announce Type: new Abstract: This paper provides a formulation of the particle flow particle filter from the perspective of variational inference. We show that the transient density used to derive the particle flow particle filter follows a time-scaled trajectory of the Fisher-Rao gradient flow in the…

  • Likelihood-Free Variational Autoencoders

    Likelihood-Free Variational Autoencoders arXiv:2504.17622v1 Announce Type: new Abstract: Variational Autoencoders (VAEs) typically rely on a probabilistic decoder with a predefined likelihood, most commonly an isotropic Gaussian, to model the data conditional on latent variables. While convenient for optimization, this choice often leads to likelihood misspecification, resulting in blurry reconstructions and poor data fidelity, especially for…

  • Robust and Scalable Variational Bayes

    Robust and Scalable Variational Bayes arXiv:2504.12528v1 Announce Type: new Abstract: We propose a robust and scalable framework for variational Bayes (VB) that effectively handles outliers and contamination of arbitrary nature in large datasets. Our approach divides the dataset into disjoint subsets, computes the posterior for each subset, and applies VB approximation independently to these posteriors.…

  • Accelerating Particle-based Energetic Variational Inference

    Accelerating Particle-based Energetic Variational Inference arXiv:2504.03158v1 Announce Type: new Abstract: In this work, we propose a novel particle-based variational inference (ParVI) method that accelerates the EVI-Im. Inspired by energy quadratization (EQ) and operator splitting techniques for gradient flows, our approach efficiently drives particles towards the target distribution. Unlike EVI-Im, which employs the implicit Euler method…

  • Variational Autoencoded Multivariate Spatial Fay-Herriot Models

    Variational Autoencoded Multivariate Spatial Fay-Herriot Models arXiv:2503.14710v1 Announce Type: new Abstract: Small area estimation models are essential for estimating population characteristics in regions with limited sample sizes, thereby supporting policy decisions, demographic studies, and resource allocation, among other use cases. The spatial Fay-Herriot model is one such approach that incorporates spatial dependence to improve estimation…

  • Variational phylogenetic inference with products over bipartitions

    Variational phylogenetic inference with products over bipartitions arXiv:2502.15110v1 Announce Type: new Abstract: Bayesian phylogenetics requires accurate and efficient approximation of posterior distributions over trees. In this work, we develop a variational Bayesian approach for ultrametric phylogenetic trees. We present a novel variational family based on coalescent times of a single-linkage clustering and derive a closed-form…

  • Federated Variational Inference for Bayesian Mixture Models

    Federated Variational Inference for Bayesian Mixture Models arXiv:2502.12684v1 Announce Type: new Abstract: We present a federated learning approach for Bayesian model-based clustering of large-scale binary and categorical datasets. We introduce a principled ‘divide and conquer’ inference procedure using variational inference with local merge and delete moves within batches of the data in parallel, followed by…

  • Variational Schr”odinger Momentum Diffusion

    Variational Schr”odinger Momentum Diffusion arXiv:2501.16675v1 Announce Type: new Abstract: The momentum Schr”odinger Bridge (mSB) has emerged as a leading method for accelerating generative diffusion processes and reducing transport costs. However, the lack of simulation-free properties inevitably results in high training costs and affects scalability. To obtain a trade-off between transport properties and scalability, we introduce…

  • Globally Convergent Variational Inference

    Globally Convergent Variational Inference arXiv:2501.08201v1 Announce Type: new Abstract: In variational inference (VI), an approximation of the posterior distribution is selected from a family of distributions through numerical optimization. With the most common variational objective function, known as the evidence lower bound (ELBO), only convergence to a local optimum can be guaranteed. In this work,…

  • Decoding the Hack behind Accurate Weather Forecasting: Variational Data Assimilation

    Decoding the Hack behind Accurate Weather Forecasting: Variational Data Assimilation Learn how to implement the variational data assimilation, with mathematical details and PyTorch for efficient implementation. Continue reading on Towards Data Science ยป Wencong Yang, PhD Go to original source