Posterior Sampling

Latest papers 63

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

Inverse Problems Conditioned on Observation Ensembles: Applications and Methods
Jan 29, 2026Zhengyan Huan, Camila Pazos, Martin Klassen +3Posterior Sampling Inverse Problems
Department of Electrical and Computer Engineering, Tufts University, Medford, Massachusetts · The NSF AI Institute for Artificial Intelligence and Fundamental Interactions · Department of Physics and Astronomy, Tufts University, Medford, Massachusetts +1
PhyloGFN: Phylogenetic inference with generative flow networks
Oct 12, 2023Mingyang Zhou, Zichao Yan, Elliot Layne +5Bayesian Inference Posterior Sampling
McGill University · McGill University, Mila · Universit´e de Montr´eal, Mila
Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors
Sep 12, 2023Prateek Jaiswal, Debdeep Pati, Anirban Bhattacharya +1Thompson Sampling Multi-Armed Bandits
Department of Quantitative Methods, Daniels School of Business, Purdue University, West Lafayette, IN 47906, USA. · Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA. · Department of Statistics, Texas A&M University, College Station, TX 77843, USA.

Jan 29, 2026J/K move · Enter open · S save

Inverse Problems Conditioned on Observation Ensembles: Applications and Methods

Zhengyan Huan, Camila Pazos, Martin Klassen, Vincent Croft+2

Department of Electrical and Computer Engineering, Tufts University, Medford, Massachusetts · The NSF AI Institute for Artificial Intelligence and Fundamental Interactions · Department of Physics and Astronomy, Tufts University, Medford, Massachusetts+1

Posterior Sampling Inverse Problems Image Inverse Problems

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We introduce a new multivariate statistical problem that we refer to as the Ensemble-conditioned Inverse Problem (EIP). The aim of EIP is to invert for an ensemble that is distributed according to the pushforward of a prior under a forward process. In high energy physics (HEP), this is related to a widely known problem called unfolding, which aims to reconstruct the true physics distribution from observations that are distorted by detector effects. The EIP also arises in full waveform inversion (FWI) and inverse imaging with unknown priors. We propose non-iterative inference-time methods that construct posterior samplers based on a new class of conditional generative models, which we call ensemble inverse generative models. For the posterior modeling, these models additionally use the ensemble information contained in the observation set on top of single observations. Unlike existing methods, our proposed methods avoid explicit and iterative use of the forward model at inference time via training across several sets of truth-observation pairs that are consistent with the same forward model, but originate from a wide range of priors. We empirically demonstrate that this training procedure can implicitly encode the likelihood model, enabling direct posterior inference for unseen priors to some degree. We benchmark the proposed method on several synthetic and real datasets in inverse imaging, HEP, and FWI. Our code is available at https://github.com/ZhengyanHuan/EIP.

Latest papers 63

Inverse Problems Conditioned on Observation Ensembles: Applications and Methods

PhyloGFN: Phylogenetic inference with generative flow networks

Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors