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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Learning Generation Orders for Masked Discrete Diffusion Models via Variational Inference
Feb 27, 2026David Fox, Sam Bowyer, Song Liu +3Masked Diffusion Models Diffusion Language Model Inference
Mistral AI
Function-Space Empirical Bayes Regularisation with Student's t Priors
Feb 25, 2026Pengcheng Hao, Ercan Engin KuruogluBayesian Neural Networks Bayesian Inference
Institute of Data and Information, Tsinghua Shenzhen International Graduate School, Shenzhen, China
Variational Inference for Bayesian MIDAS Regression
Feb 23, 2026Luigi SimeoneVariational Inference
Independent Researcher
VariBASed: Variational Bayes-Adaptive Sequential Monte-Carlo Planning for Deep Reinforcement Learning
Feb 21, 2026Joery A. de Vries, Jinke He, Yaniv Oren +3Bayesian RL Sample-Efficient RL
Trent AI Ltd, London, United Kingdom · EEMCS, Delft University of Technology, Delft, the Netherlands
Vectorized Bayesian Inference for Latent Dirichlet-Tree Allocation
Feb 21, 2026Zheng Wang, Nizar BouguilaTopic Modeling Bayesian Inference
Concordia Institute for Information Systems Engineering Concordia University Montreal, QC H3G 1M8, Canada
Stochastic Gradient Variational Inference with Price's Gradient Estimator from Bures-Wasserstein to Parameter Space
Feb 21, 2026Kyurae Kim, Qiang Fu, Yi-An Ma +2Variational Inference
University of Pennsylvania, Philadelphia, U.S. · Yale University, New Haven, U.S. · University of California San Diego, San Diego, U.S. +1
Variational Distributional Neuron
Feb 20, 2026Yves RuffenachVariational Autoencoders Variational Inference
Conservatoire National des Arts et Métiers
Variational inference via radial transport
Feb 19, 2026Luca Ghafourpour, Sinho Chewi, Alessio Figalli +1Variational Inference
ETH Zürich · Cambridge · Yale University
Variational Bayesian Flow Network for Graph Generation
Jan 30, 2026Yida Xiong, Jiameng Chen, Xiuwen Gong +3Graph Generation Molecular Graph Generation
School of Computer Science, Wuhan University, Wuhan, China · Department of Computing, Macquarie University, Sydney, Australia · Griffith University, Gold Coast, Australia +1
Recursive Entropic Variational Inference for Nonlinear State-Space Models
Nov 19, 2025Hany Abdulsamad, Ángel F. García-Fernández, Simo SärkkäBayesian Filtering Variational Inference
Amsterdam Machine Learning Lab University of Amsterdam, Netherlands · Information Processing and Telecommunications Center Universidad Politécnica de Madrid, Spain · ELLIS Institute Finland Department of Electrical Engineering and Automation Aalto University, Finland
Mode Collapse of Mean-Field Variational Inference
Oct 20, 2025Shunan Sheng, Bohan Wu, Alberto González-SanzVariational Inference Mode Collapse
Columbia University, Department of Statistics · Department of Statistics, Columbia University
Neural Variational Cut Posteriors without Upstream Data
Oct 11, 2025Jiafang Song, Sandipan Pramanik, Abhirup DattaBayesian Inference Variational Inference
Department of Biostatistics, Johns Hopkins University, USA
A Generalized Tangent Approximation based Variational Inference Framework for Strongly Super-Gaussian Likelihoods
Apr 7, 2025Somjit Roy, Pritam Dey, Debdeep Pati +1Bayesian Inference Variational Inference
Department of Statistics, Texas A&M University, College Station, TX 77843 · Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706
Sublinear Variational Optimization of Gaussian Mixture Models with Millions to Billions of Parameters
Jan 21, 2025Sebastian Salwig, Till Kahlke, Florian Hirschberger +2Gaussian Mixture Models Variational Inference
Machine Learning Lab, DMPA, Faculty VI Carl von Ossietzky University Oldenburg 26129 Oldenburg, Germany · Artificial Intelligence Lab, Department of Computer Science, Faculty of Mathematics, Computer Science and Physics Innsbruck Research Center for Artificial Intelligence University of Innsbruck 6020 Innsbruck, Austria · Data Analytics and AI, Faculty 3 Frankfurt University of Applied Sciences 60318 Frankfurt am Main, Germany
Variational Model Merging for Pareto Front Estimation in Multitask Finetuning
Dec 11, 2024Hugo Monzón Maldonado, Nico Daheim, Thomas Möllenhoff +2Bayesian Inference Multi-Task Learning
RIKEN Center for AI Project, Japan · Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science, Technical University of Darmstadt and National Research Center for Applied Cybersecurity ATHENE, Germany · National Research Center for Applied Cybersecurity ATHENE, Germany
Inclusive KL Gradient Flows: Otto-Wasserstein, Fisher-Rao-Gaussian, and Local-Estimator Dynamics
Oct 31, 2024Jia-Jie ZhuWasserstein Gradient Flows Variational Inference
Department of Mathematics, KTH Royal Institute of Technology, Lindstedtsvägen 25, 114 28, Stockholm, Sweden
Predictive variational inference: Learn the predictively optimal posterior distribution
Oct 18, 2024Jinlin Lai, Antonio Linero, Yuling YaoModel Misspecification Variational Inference
College of Information and Computer Sciences, University of Massachusetts Amherst · Department of Statistics and Data Sciences, University of Texas, Austin
Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descent
Sep 13, 2024Sayan Banerjee, Krishnakumar Balasubramanian, Promit GhosalKernel Methods Variational Inference
Department of Statistics and Operations Research, University of North Carolina, Chapel Hill. · Department of Statistics, University of California, Davis. · Department of Statistics, University of Chicago.
GFlowNets and variational inference
Oct 2, 2022Esmeralda S. Whitammer, Salem Lahlou, Tristan Deleu +5Generative Flow Networks RL for Generative Models
Mila, Universit´e de Montr´eal · Google Research · Mila, Universit´e de Montr´eal, CIFAR

Feb 27, 2026J/K move · Enter open · S save

Learning Generation Orders for Masked Discrete Diffusion Models via Variational Inference

David Fox, Sam Bowyer, Song Liu, Laurence Aitchison+2

Mistral AI

Masked Diffusion Models Diffusion Language Model Inference Diffusion Language Model Decoding Variational Inference

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Masked discrete diffusion models (MDMs) are a promising new approach to generative modelling, offering the ability for parallel token generation and therefore greater efficiency than autoregressive counterparts. However, achieving an optimal balance between parallel generation and sample quality remains an open problem. Current approaches primarily address this issue through fixed, heuristic parallel sampling methods. There exist some recent learning based approaches to this problem, but its formulation from the perspective of variational inference remains underexplored. In this work, we propose a variational inference framework for learning parallel generation orders for MDMs. As part of our method, we propose a parameterisation for the approximate posterior of generation orders which facilitates parallelism and efficient sampling during training. Using this method, we conduct preliminary experiments on the GSM8K dataset, where our method performs competitively against heuristic sampling strategies in the regime of highly parallel generation. For example, our method achieves 33.1% accuracy with an average of only only 4 generation steps, compared to 23.7-29.0% accuracy achieved by standard competitor methods in the same number of steps. We believe further experiments and analysis of the method will yield valuable insights into the problem of parallel generation with MDMs.