Posterior Sampling

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18 papers in the last four weeks, up 260% on the four weeks before. 0.1% of all new papers.

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BridgeTwist: Twisting Schrödinger Bridges for Training-Free Conditional Sampling
Oct 13, 2026Mengyu Li, Qianqian Qu, Jun S. LiuConditional Generative Modeling Posterior Sampling
Department of Statistics and Data Science, Tsinghua University, Beijing, China
SCOPE Before You Sample: Conformal Scores Guide Posterior Exploration for Fast and Calibrated Bayesian Predictive Inference
Oct 12, 2026Zahra Moslemi, Ziyi Liang, Babak ShahbabaBayesian Inference Posterior Sampling
University of California, Irvine · University of California, Santa Barbara
Sampling from Neural Operator Priors to Solve Partial Differential Equations
Oct 12, 2026Andrew Millard, Joshua Murphy, Zheng ZhaoPDE Solving Posterior Sampling
Department of Computer and Information Science, Linköping University, Sweden. · Department of Political Science, Trinity College Dublin, Ireland.
Spanning Diffusion and Flow-based Posterior Sampling via Exact Gaussian Conditioning
Oct 9, 2026Teo Deveney, Lea Bogensperger, Hok Shing WongPosterior Sampling Diffusion Posterior Sampling
University of Bath · University of Zurich · LMU Munich
Amortised Inference through One-Step Implicit Sampling
Oct 8, 2026Vincent Pauline, Kirill Tamogashev, Arran Carter +3Amortized Inference Posterior Sampling
Technical University of Munich · Munich Center for Machine Learning · University of Edinburgh +1
Recovery Guarantees for Posterior Sampling of One-Bit Compressed Sensing
Oct 8, 2026Jing Ma, Yujia Wu, Zhaoqiang LiuCompressed Sensing Sample Complexity
University of Electronic Science and Technology of China
Twist Flow for Inverse Problems
Oct 7, 2026Shiqin Zeng, Zijun Deng, Felix J. HerrmannBayesian Inverse Problems Posterior Sampling
Georgia Institute of Technology
Direct Intermediate Initialization for Tilted Diffusion Samplers
Oct 5, 2026Gregory D. BellchambersPosterior Sampling Diffusion-Based Inverse Problems
PhysicsX Ltd. London, EC2A 4DQ
A Unified Framework for Bayesian Data Assimilation with Generative Models and Observation Interpolants
Oct 2, 2026Nikolaj T. Mücke, Benjamin SanderseData Assimilation Posterior Sampling
Scientific Computing, Centrum Wiskunde & Informatica · Faculty of Civil Engineering and Geosciences, Delft University of Technology · Centre for Analysis, Scientific Computing and Applications, Eindhoven University of Technology
Flow Matching for Fast Posterior Sampling in Bayesian Inverse Problems
Oct 1, 2026Jan Blechschmidt, Oliver G. Ernst, Moritz Poguntke +1Posterior Sampling Bayesian Inverse Problems
Posterior sampling by source-space MCMC via prior-based few-step transport maps
Oct 1, 2026Hoang Phuc Hau Luu, Marcelo Hartmann, Zhongjian WangMarkov Chain Monte Carlo Bayesian Inference
Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore. · Department of Computer Science, Faculty of Science, University of Helsinki, Finland.
Specificity-Aware Diffusion Steering via Variance-Reduced Sequential Monte Carlo
Sep 30, 2026Luran Wang, Linrui Ma, Hannes Stärk +1Diffusion Model Guidance Diffusion Guidance
CSAIL, Massachusetts Institute of Technology
SNaP: One-Step Posterior Sampling for Noisy Inverse Problems
Sep 28, 2026Shirin Shoushtari, Edward P. Chandler, Xiao Shi +1Flow Matching MeanFlow
WashU · UW-Madison
Minimax-Optimality of Posterior Sampling for Reinforcement Learning
Sep 27, 2026Taewon Goo, Kihyuk HongRegret Minimization in RL Markov Decision Processes
KAIST
Nuclear Norm-Regularized Bayesian Matrix Completion
Sep 24, 2026Calvin TolbertNuclear Norm Minimization Posterior Sampling
School of Operations Research and Information Engineering Cornell University
Local Geometric Mixing via Dobrushin Contraction with Applications to Diffusion Path Monte Carlo and the Proximal Sampler
Sep 23, 2026Stefan OberdörsterMarkov Chain Monte Carlo Posterior Sampling
Institute for Applied Mathematics, University of Bonn
Auditing Bayesian Graph Alignment: Diagnostic Comparisons and Reference Failure
Sep 19, 2026Melika Gorgi, Kourosh MirsohiMarkov Chain Monte Carlo Benchmark Auditing
Center for Complex Biological Systems University of California, Irvine Irvine, CA 92697, USA · Department of Computer Science University of California, Irvine Irvine, CA 92697, USA
How Many Posterior Samples? Calibrated Stopping for Adaptive Sensing
Sep 18, 2026Vincent Corlay, Andriy EnttselActive Perception Optimal Stopping
Mitsubishi Electric R&D Centre Europe
Aggregated Posterior Predictive Checks for Generative Modeling
Sep 17, 2026Shweta Dutta, Gemma E. MoranGenerative Modeling Latent Variable Models
Department of Statistics, Rutgers University
FlowSGS: Improving Flow Priors for Inverse Imaging with Stochastic Interpolants
Sep 17, 2026Tianao Li, Xinhui Qian, Emma AlexanderFlow Matching Markov Chain Monte Carlo
Department of Computer Science, Northwestern University · NSF-Simons AI Institute for the Sky (SkAI) · Department of Statistics and Data Science, Northwestern University
Quenched Ensemble Sampling
Sep 14, 2026David YallupBayesian Inference Posterior Sampling
Kavli Institute for Cosmology Cambridge Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge, CB3 0HA, UK
GRPO-QPS: Target-Preserving Reinforcement Learning for Quantum Posterior Sampling
Sep 13, 2026Yufeng Wang, Parivesh Priye, Lu Wei +1Markov Chain Monte Carlo Quantum State Tomography
Stony Brook University · Georgia Institute of Technology · Westlake University
High-Magnetization Sampling at Low Temperatures: Ising Models and Bayesian Sparse Linear Regression
Sep 8, 2026Syamantak Kumar, Purnamrita Sarkar, Kevin Tian +1Sparse Recovery Ising Model
University of Texas at Austin
Learning Informative Prior with Infinite-Dimensional Continuous Normalizing Flow for Bayesian Inverse Problem
Sep 3, 2026Yang Zhao, Junxiong Jia, Tao ZhouNormalizing Flows Bayesian Inverse Problems
School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China · Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing, 100190, China
Posterior Tempering Explains Variance Inflation in Linear and Generalized Linear Thompson Sampling
Sep 2, 2026Prateek Jaiswal, Debdeep Pati, Anirban Bhattacharya +1Thompson Sampling Multi-Armed Bandits
Daniels School of Business, Purdue University. · Department of Statistics, University of Wisconsin-Madison. · Department of Statistics, Texas A&M University.
Picard Proximal Monte Carlo for Parallel Bayesian Imaging with Score-Based Generative Priors
Aug 18, 2026Deliang Wei, Evan Bell, Wenhan Guo +2Posterior Sampling Bayesian Inverse Problems
Johns Hopkins University · University of California, Los Angeles
FLARE MCMC: Fidelity-based Layer-Adaptive REcursive proposals for MCMC
Aug 13, 2026Harini Venkatesan, Christian Shelton, Ming-Feng Ho +2Markov Chain Monte Carlo Posterior Sampling
Department of Computer Science and Engineering, University of California, Riverside · Leinweber Center for Theoretical Physics, University of Michigan · Department of Physics and Astronomy, University of California, Riverside
Generator-Guided Inverse Sampling for Lévy-Driven Generative Models
Aug 11, 2026Tianfu Qi, Jun Wang, Jun ZhangPosterior Sampling Generative Modeling
National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu 611731, China · Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, China
Path-dependent Discrete Amortized Inference
Aug 9, 2026Tiago da Silva, Esmeralda S. Whitammer, Salem LahlouMarkov Decision Processes Latent Dynamics Modeling
1MBZUAI · University of Edinburgh · 3CIFAR Fellow
Guiding Posterior Exploration with Optimizer-Derived Geometry
Jul 28, 2026Moritz Schlager, Emanuel Sommer, Thomas Möllenhoff +1Bayesian Neural Networks Markov Chain Monte Carlo
TUM, MCML · LMU Munich, MCML · RIKEN AIP

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BridgeTwist: Twisting Schrödinger Bridges for Training-Free Conditional Sampling

Mengyu Li, Qianqian Qu, Jun S. Liu

Department of Statistics and Data Science, Tsinghua University, Beijing, China

Conditional Generative Modeling Posterior Sampling Schrödinger Bridges Markov Chain Monte Carlo

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Generative models are often required to serve many downstream conditional tasks. In the training-free setting, a pretrained unconditional model is reused as a prior and combined with a task-specific likelihood to sample from the corresponding posterior. While such training-free samplers have been developed for diffusion and flow models, their counterpart for Schrödinger bridge (SB) models remains unexplored, despite the growing appeal of SBs as flexible stochastic transport models between general source and target distributions. We propose BridgeTwist, a training-free conditional sampler for pretrained SB generative models, formulated as a tempered twisted sequential Monte Carlo scheme. Its twist is a closed-form plug-in surrogate, obtained by combining the velocity and score fields exposed by SB pretraining into an endpoint predictor and evaluating the likelihood there. Since this surrogate is less reliable near the source, we temper the twist with a time-varying exponent, downweighting its influence at early steps. We further introduce a bridge-shaped inference-time diffusion schedule that concentrates the proposal noise in the interior of the path. Telescoping importance weights then cancel all intermediate twist factors, yielding an asymptotically exact sampler for the posterior. Empirically, BridgeTwist outperforms training-free baselines on class-conditional and text-prompted image generation, and is competitive with specialized inverse problem solvers on inpainting, deblurring, and super-resolution.