Variational Inference

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Period ending 2026-09-21

2 new papers

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Period ending 2026-09-14

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Period ending 2026-09-07

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104 papers

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Efficient Learning of Deep State Space Models via Importance Smoothing
May 20, 2026John-Joseph Brady, Nikolas Nusken, Yunpeng LiState Space Models Variational Inference
Closed-form predictive coding via hierarchical Gaussian filters
May 19, 2026Aleksandrs Baskakovs, Sylvain Estebe, Kenneth Enevoldsen +3Predictive Coding Variational Inference
Generative Recursive Reasoning
May 19, 2026Junyeob Baek, Mingyu Jo, Minsu Kim +3Recursion Latent Trajectories
Distributionally Robust Control via Stein Variational Inference for Contact-Rich Manipulation
May 18, 2026Hrishikesh Sathyanarayan, Victor Vantilborgh, Harish Ravichandar +2Generalizable Robotic Manipulations Contact-Rich Manipulation
Variational Autoregressive Networks with probability priors
May 15, 2026Piotr Białas, Piotr Korcyl, Tomasz Stebel +1Two-Dimensional Ising Model Variational Inference
$f$ -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 Time-Step-Wise Learnable Surrogate Gradient Function
A Mixture Autoregressive Image Generative Model on Quadtree Regions for Gaussian Noise Removal via Variational Bayes and Gradient Methods
May 12, 2026Shota Saito, Yuta Nakahara, Kohei Horinouchi +3Image Denoising Autoregressive Image Generation
On the Approximation Complexity of Matrix Product Operator Born Machines
May 12, 2026Chao Li, Zerui Tao, Yuchen Cong +2Tensor Networks Variational Inference
Variational Inference for Lévy Process-Driven SDEs via Neural Tilting
May 11, 2026Yaman Kindap, Manfred Opper, Benjamin Dupuis +2Variational Inference Stochastic Differential Equations
Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights
May 11, 2026Jixiang Qing, Henry Moss, Matthias SachsGaussian Process Active Learning
Learning stochastic multiscale models through normalizing flows
May 10, 2026Anan Saha, Arnab GangulyMultiscale Dynamics Stochastic Dynamics
A Game Theoretic Free Energy Analysis of Higher Order Synergy in Attention Heads of Large Language Models
May 10, 2026Djamel BouchaffraFree Energy Principle Synergy
Learning to Explore: Scaling Agentic Reasoning via Exploration-Aware Policy Optimization
May 9, 2026Xingyuan Hua, Sheng Yue, Ju RenAgentic Reinforcement-Learning Framework Agentic Reasoning
Beyond "I cannot fulfill this request": Alleviating Rigid Rejection in LLMs via Label Enhancement
May 8, 2026Ying Zhang, Congyu Qiao, Xin Geng +1Large Language Model Safety Rejection
SGD for Variational Inference: Tackling Unbounded Variance via Preconditioning and Dynamic Batching
May 8, 2026Hippolyte Labarrière, Cesare Molinari, Silvia Villa +1Variational Inference Mini-Batch Stochastic Gradient Descent
PISTO: Proximal Inference for Stochastic Trajectory Optimization
May 8, 2026Hongzhe Yu, Zinuo Chang, Yongxin ChenTrajectory Optimization Proximal Policy Optimization
Variational Smoothing and Inference for SDEs from Sparse Data with Dynamic Neural Flows
May 7, 2026Yu Wang, Arnab GangulyStochastic Differential Equations Variational Inference
Amortized Variational Inference for Joint Posterior and Predictive Distributions in Bayesian Uncertainty Quantification
May 5, 2026Nan Feng, Xun HuanVariational Inference Posterior
Distributed Deep Variational Approach for Privacy-preserving Data Release
May 4, 2026Zahir Alsulaimawi, Huaping LiuFederated Learning Distributional Learning
Deep Variational Inference Symbolic Regression
May 1, 2026James Butterworth, Gevik Grigorian, Alejandro DiazDelaOSymbolic Regression Variational Inference
A Collective Variational Principle Unifying Bayesian Inference, Game Theory, and Thermodynamics
Apr 30, 2026Djamel Bouchaffra, Faycal Ykhlef, Mustapha Lebbah +1Game Theory Multi-Agent Systems
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 +3Time Series Spatio-Temporal Transformers
Variational Neural Belief Parameterizations for Robust Dexterous Grasping under Multimodal Uncertainty
Apr 28, 2026Clinton Enwerem, Shreya Kalyanaraman, John S. Baras +1Grasp Generation Belief Space
Occam's Razor is Only as Sharp as Your ELBO
Apr 28, 2026Ethan Harvey, Michael C. HughesModel Selection Bayesian
Learning with Embedded Linear Equality Constraints via Variational Bayesian Inference
Apr 27, 2026Matthew Marsh, Benoît Chachuat, Antonio del Rio ChanonaBayesian Neural Networks Variational Inference
Heterogeneous Variational Inference for Markov Degradation Hazard Models: Discretized Mixture with Interpretable Clusters
Apr 27, 2026Takato YasunoMixture Models Variational Inference
Even More Guarantees for Variational Inference in the Presence of Symmetries
Apr 23, 2026Lena Zellinger, Antonio VergariVariational Inference Variational
Symmetry Guarantees Statistic Recovery in Variational Inference
Apr 20, 2026Daniel Marks, Dario Paccagnan, Mark van der WilkVariational Inference Variational
How to Approximate Inference with Subtractive Mixture Models
Apr 17, 2026Lena Zellinger, Nicola Branchini, Lennert De Smet +3Variational Inference Mixture Models
A Synonymous Variational Perspective on the Rate-Distortion-Perception Tradeoff
Apr 16, 2026Zijian Liang, Kai Niu, Changshuo Wang +2Rate-Distortion Theory Context Compression
Structured SIR: Efficient and Expressive Importance-Weighted Inference for High-Dimensional Image Registration
Mar 18, 2026Ivor J. A. Simpson, Neill D. F. CampbellImage Registration Variational Inference
VaSST: Variational Inference for Symbolic Regression using Soft Symbolic Trees
Feb 27, 2026Somjit Roy, Pritam Dey, Bani K. MallickSymbolic Regression Variational Inference
Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning
Feb 1, 2026Pietro Carlotti, Nevena Gligić, Arya FarahiDensity Ratio Estimation Distribution Shifts
Variational Approach for Job Shop Scheduling
Jan 30, 2026Seung Heon Oh, Jiwon Baek, Hyunjin Oh +3Variational Inference Variational
Meta-RL with Bayesian Linear Task Models
Dec 24, 2025Jingyang You, Hanna KurniawatiDeep Reinforcement Learning Variational Inference
Recursive Entropic Variational Inference for Nonlinear State-Space Models
Nov 19, 2025Hany Abdulsamad, Ángel F. García-Fernández, Simo SärkkäVariational Inference State Space Models
Representation Learning for Equivariant Inference with Guarantees
May 26, 2025Daniel Ordoñez-Apraez, Vladimir Kostić, Alek Fröhlich +3Equivariance Geometric Deep Learning
Inclusive KL Gradient Flows: Otto-Wasserstein, Fisher-Rao-Gaussian, and Local-Estimator Dynamics
Oct 31, 2024Jia-Jie ZhuWasserstein Gradient Flows Variational Inference
Predictive variational inference: Learn the predictively optimal posterior distribution
Oct 18, 2024Jinlin Lai, Antonio Linero, Yuling YaoVariational Inference Posterior
Joint Bayesian Parameter and Model Order Estimation for Low-Rank Probability Mass Tensors
Oct 8, 2024Joseph K. Chege, Arie Yeredor, Martin HaardtBayesian Low-Rank Factorization
A locally statistical active contour model for SAR image segmentation can be solved by denoising algorithms
Jan 10, 2024Guangming LiuRobust Segmentation Synthetic Aperture Radar
GFlowNets and variational inference
Oct 2, 2022Esmeralda S. Whitammer, Salem Lahlou, Tristan Deleu +5Variational Inference Generative Flow Networks
A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel
Nov 21, 2021Yindong Chen, Yiwei Wang, Lulu Kang +1Maximum Mean Discrepancy Variational Inference
A Generalized Tangent Approximation based Variational Inference Framework for Strongly Super-Gaussian Likelihoods
Date pendingSomjit Roy, Pritam Dey, Debdeep Pati +1Variational Inference Variational

May 20, 2026 · cs.LGJ/K move · Enter open · S save

Efficient Learning of Deep State Space Models via Importance Smoothing

John-Joseph Brady, Nikolas Nusken, Yunpeng Li

Latent state space systems are ubiquitous in statistical modelling, arising naturally when time series are observed through noisy measurements. However, training deep state space models (DSSMs) at scale remains difficult. Two largely distinct strategies have emerged for training DSSMs. The first, auto-encoding DSSMs, trains generative models by optimising a variational lower bound. The second backpropagates through the outputs of classical sequential Monte Carlo (SMC) algorithms. Such approaches can train DSSMs for both discriminative and generative tasks, but their inherently sequential forward passes scale poorly on modern hardware. We propose \emph{parallel variational Monte Carlo} (PVMC), a new training method that bridges these paradigms and robustly trains DSSMs for both discriminative and generative tasks. Across a set of benchmark experiments, PVMC matches or exceeds state-of-the-art performance while training

10\times

faster than the fastest competing SMC-based approach.

State Space Models Variational Inference Generative Models Single Forward Pass

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