Variational Inference

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Latest papers 139

14 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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VEDAL: Variational Error-Driven Asynchronous Learning for 3D Gaussian Splatting Pruning
Jun 1, 2026Aoduo Li, Jiancheng Li, Huan Ye +53DGS Compression 3D Gaussian Splatting
Guangdong University of Technology · Huizhou Boluo Power Supply Bureau, Guangdong Power Grid Co., Ltd. · Shenzhen Polytechnic University +1
Flow-Transformed Implicit Processes for Function-Space Variational Inference
Jun 1, 2026Luis A. Ortega, Andrés R. Masegosa, Thomas D. NielsenBayesian Inference Variational Inference
Aalborg University
Wasserstein Contraction of Coordinate Ascent Variational Inference
May 28, 2026Rocco Caprio, Adrien Corenflos, Sam PowerBayesian Inference Variational Inference
University of Warwick and Bocconi University. · University of Warwick. · University of Bristol.
Composing Non-Conjugate Factor Graphs with Closed-Form Variational Inference
May 28, 2026Mykola Lukashchuk, Kyrylo Yemets, Wouter M. Kouw +4Factor Graph Optimization Mixture-of-Experts Inference
Eindhoven University of Technology, the Netherlands · Lviv Polytechnic National University, Lviv, Ukraine · Lazy Dynamics, Utrecht, the Netherlands +1
Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models
May 27, 2026Simon Queric, Cédric Vincent-Cuaz, Charles Bouveyron +1Model Selection Latent Variable Models
Universit´e Cˆote d’Azur, Inria, CNRS LJAD, Maasai Nice, France · EPFL Lausanne, Switzerland · Universit´e Cˆote d’Azur, Inria, CNRS CEPAM, Maasai Nice, France
Soft Specialists: $α$ -Rényi Ensembles for Uncertainty-Aware LLM Post-Training
May 26, 2026Paula Cordero-Encinar, Georgy Tyukin, Andrew B. DuncanLLM Uncertainty Estimation Language Model Ensembles
Department of Mathematics, Imperial College London · Bessemer AI
Variational Inference for Evidential Deep Learning
May 26, 2026Jiawei Tang, Xinyan Du, Hui Liu +2Evidential Deep Learning Uncertainty Quantification
School of Computer Science and Engineering Southeast University Nanjing 210096, China · School of Computing Information Sciences Saint Francis University2026 Hong Kong SAR, China · CityDepartmentUniversityof Computerof Hong KongScience Hong Kong SAR, China
Amortized Factor Inference Networks for Posterior Inference
May 26, 2026Joohwan Ko, Justin DomkeAmortized Inference Bayesian Inference
Manning College of Information and Computer Sciences University of Massachusetts Amherst
Unbiased Diffusion Variational Inversion via Principled Posterior Matching
May 24, 2026Weimin Bai, Yuxuan Gu, Yifei Wang +2Diffusion Model Inversion Bayesian Inverse Problems
Peking University
TUBE: Tangent Upper Bound on Evidence for Discrete Diffusion Language Models
May 22, 2026Arseny Ivanov, Sergei Kholkin, Vladislav Gromadskii +3Masked Diffusion Models Autoregressive Language Modeling
AXXX, Russia · Applied AI Institute, Moscow, Russia · MIRAI, Russia
Onsager-Machlup Posterior Transport for Deep Gaussian Processes
May 22, 2026Jian Xu, Delu Zeng, John Paisley +1Gaussian Processes Variational Inference
RIKEN iTHEMS · RIKEN AIP · South China University of Technology +1
Efficient Learning of Deep State Space Models via Importance Smoothing
May 20, 2026John-Joseph Brady, Nikolas Nusken, Yunpeng LiVariational Inference State Space Models
Centre for Oral, Clinical and Translational Sciences, King’s College London, London, United Kingdom · Department of Mathematics, King’s College London, London, United Kingdom
Closed-form predictive coding via hierarchical Gaussian filters
May 19, 2026Aleksandrs Baskakovs, Sylvain Estebe, Kenneth Enevoldsen +3Predictive Coding Variational Inference
Center for Humanities Computing Aarhus University Aarhus, Denmark · Interacting Minds Center Aarhus University Aarhus, Denmark
Distributionally Robust Control via Stein Variational Inference for Contact-Rich Manipulation
May 18, 2026Hrishikesh Sathyanarayan, Victor Vantilborgh, Harish Ravichandar +2Contact-Rich Robotic Manipulation Robust Control
Yale University, New Haven, CT, USA · Ghent University, Belgium · Georgia Institute of Technology, Atlanta, GA, USA +1
Variational Autoregressive Networks with probability priors
May 15, 2026Piotr Białas, Piotr Korcyl, Tomasz Stebel +1Autoregressive Generation Physics-Informed ML
Institute of Applied Computer Science, Jagiellonian University, ul. Łojasiewicza 11, 30-348, Kraków, Poland · Institute of Theoretical Physics, Jagiellonian University, ul. Łojasiewicza 11, 30-348, Kraków, Poland · Doctoral School of Exact and Natural Sciences, Jagiellonian University, ul. Łojasiewicza 11, 30-348, Kraków, Poland
$f$ -Trajectory Balance: A Loss Family for Tuning GFlowNets, Generative Models, and LLMs with Off- and On-Policy Data
May 14, 2026Jake Fawkes, Jason HartfordGenerative Flow Networks Variational Inference
Department of Statistics, University College London, UK · Valence Labs, London, UK · Recursion, London, UK.
Variational Inference for Lévy Process-Driven SDEs via Neural Tilting
May 11, 2026Yaman Kindap, Manfred Opper, Benjamin Dupuis +2Stochastic Differential Equations Bayesian Inference
1Imperial College London, UK · 2Technical University of Berlin, Germany · 3INRIA, CNRS, Département d’Informatique de l’Ecole Normale Supérieure / PSL, France
Efficient LLM Reasoning via Variational Posterior Guidance with Efficiency Awareness
May 10, 2026Zizhao Chen, Yuying Li, Siting Lin +1LLM Inference Efficiency Efficient Language Model Reasoning
Guangdong University of Foreign Studies, Guangzhou, China.
Quantitative Local Convergence of Mean-Field Stein Variational Gradient Flow
May 10, 2026Lénaïc Chizat, Maria Colombo, Roberto Colombo +1Dynamical Systems Optimization Convergence Analysis
Direct Bethe Free Energy Minimization for Bayesian Neural Networks
May 8, 2026Pavel ProchazkaBayesian Neural Networks Variational Inference
Cisco Inc.
SGD for Variational Inference: Tackling Unbounded Variance via Preconditioning and Dynamic Batching
May 8, 2026Hippolyte Labarrière, Cesare Molinari, Silvia Villa +1Stochastic Optimization Convergence Variational Inference
MaLGa, DIBRIS Università degli Studi di Genova Genoa, Italy · MaLGa, DIMA Università degli Studi di Genova Genoa, Italy · MaLGa, DIBRIS, IIT Università degli Studi di Genova Genoa, Italy
PISTO: Proximal Inference for Stochastic Trajectory Optimization
May 8, 2026Hongzhe Yu, Zinuo Chang, Yongxin ChenTrajectory Optimization Trajectory Generation
School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA · School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA
Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks
May 8, 2026Ryoichiro Agata, Tomohisa OkazakiBayesian Inverse Problems Physics-Informed ML
Japan Agency for Marine-Earth Science and Technology, Japan · Disaster Prevention Research Institute, Kyoto University, Japan · RIKEN Center for Advanced Intelligence Project, Japan
Variational Smoothing and Inference for SDEs from Sparse Data with Dynamic Neural Flows
May 7, 2026Yu Wang, Arnab GangulyStochastic Differential Equations Bayesian Inference
Department of Mathematics Louisiana State University
Amortized Variational Inference for Joint Posterior and Predictive Distributions in Bayesian Uncertainty Quantification
May 5, 2026Nan Feng, Xun HuanUncertainty Quantification Amortized Inference
Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, United States
Deep Variational Inference Symbolic Regression
May 1, 2026James Butterworth, Gevik Grigorian, Alejandro DiazDelaOUncertainty Quantification Variational Inference
Clinical Operational Research Unit University College London London, United Kingdom · Department of Computer Science University College London London, United Kingdom
Robust volatility updates for Hierarchical Gaussian Filtering
May 1, 2026Christoph Mathys, Nicolas Legrand, Peter Thestrup Waade +2Bayesian Filtering Hierarchical Bayesian Modeling
Interacting Minds Centre, Aarhus University, 8000 Aarhus C, Denmark · Centre for Humanities Computing, Aarhus University, 8000 Aarhus C, Denmark · Translational Neuromodeling Unit, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland +1
A Collective Variational Principle Unifying Bayesian Inference, Game Theory, and Thermodynamics
Apr 30, 2026Djamel Bouchaffra, Faycal Ykhlef, Mustapha Lebbah +1Nash Equilibrium Cooperative Game Theory
1DAVID Lab, University of Paris-Saclay, UVSQ, 78035 Versailles, France. · 2TSAM, ASM, Centre for Development of Advanced Technologies, Algiers, Algeria. · 3LIPN, UMR CNRS 7030, Sorbonne Paris Nord University, Villetaneuse, France.
Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework
Apr 29, 2026Zhangzhi Xiong, Haoyi Wu, You Wu +3Transformer Conditional Random Fields
School of Information Science and Technology, ShanghaiTech University
Fitting Large Nonlinear Mixed Effects Models Using Variational Expectation Maximization
Apr 28, 2026Mohamed Tarek, Pedro AfonsoExpectation-Maximization Variational Inference
PumasAI, USA

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VEDAL: Variational Error-Driven Asynchronous Learning for 3D Gaussian Splatting Pruning

Aoduo Li, Jiancheng Li, Huan Ye, Hongjian Xu+4

Guangdong University of Technology · Huizhou Boluo Power Supply Bureau, Guangdong Power Grid Co., Ltd. · Shenzhen Polytechnic University+1

3DGS Compression 3D Gaussian Splatting 3D Gaussian Pruning Variational Inference Neural Network Pruning

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3D Gaussian Splatting (3DGS) achieves remarkable novel view synthesis quality with real-time rendering, yet suffers from excessive memory consumption due to millions of Gaussian primitives. Existing pruning methods rely on heuristic importance scores or synchronous batch updates, leading to suboptimal compression and training instability. We propose VEDAL, a principled framework that formulates Gaussian pruning as variational free energy minimization. Our approach introduces (1) a prediction-error gating mechanism that asynchronously activates pruning based on per-Gaussian reconstruction uncertainty, and (2) a variational uncertainty head that models pruning decisions as latent variables with learnable priors. The free energy objective naturally balances reconstruction fidelity against model complexity through an information-theoretic lens. Extensive experiments on Mip-NeRF 360, Tanks&Temples, and Deep Blending demonstrate that VEDAL achieves 5.2x compression with only 0.31 dB PSNR drop, outperforming PUP 3D-GS by +0.05 dB at a higher compression ratio and LightGaussian by +0.35 dB at comparable quality, while maintaining real-time rendering at 185 FPS.