Maximum Marginal Likelihood Estimation

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11 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Feature Space Adaptation for Effortless Gaussian Process Flows
Oct 8, 2026Thomas Cowperthwaite, Louis Sharrock, Lachlan Astfalck +1Maximum Marginal Likelihood Estimation Gaussian Processes
University of Cambridge · University College London · University of New South Wales +1
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
ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling
Jul 21, 2026Chirag Vashist, Ke LiImage Generation One-Step Generative Modeling
APEX Lab · Simon Fraser University · Simon Fraser University, Amii and CIFAR
Gradient-Flow Optimization as Dynamic Random-Effects Inference: Testing and Early Stopping with Applications to Deep Learning
May 27, 2026Minhao Yao, Ruoyu Wang, Xihong Lin +2Early Stopping Neural Network Training Dynamics
Centre for Biomedical Data Science, Duke-NUS Medical School, National University of Singapore · Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA · Institute of Natural Sciences, MOE-LSC, School of Mathematical Sciences, CMA-Shanghai, SJTU-Yale2026 Joint Center of Biostatistics and Data Science, Shanghai Jiao Tong University +1
One Pass Is Not Enough: Recursive Latent Refinement for Generative Models
May 14, 2026Mehdi Esmaeilzadeh, Alexia Jolicoeur-Martineau, Chirag Vashist +1Generative Modeling Image Generation Evaluation
1Simon Fraser University · 2Independent
Variational Approximated Restricted Maximum Likelihood Estimation for Spatial Data
Apr 8, 2026Debjoy ThakurMaximum Marginal Likelihood Estimation Variational Inference
Washington University in St. Louis, USA · Ahmedabad University, India
New insights into Elo algorithm for practitioners and statisticians
Apr 4, 2026Leszek SzczecinskiMaximum Marginal Likelihood Estimation Sports Outcome Prediction
Implicit Maximum Likelihood Estimation for Real-time Generative Model Predictive Control
Mar 14, 2026Grayson Lee, Minh Bui, Shuzi Zhou +3Maximum Marginal Likelihood Estimation Model Predictive Control
School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.
From Flow to One Step: Real-Time Multi-Modal Trajectory Policies via Implicit Maximum Likelihood Estimation-based Distribution Distillation
Mar 10, 2026Ju Dong, Liding Zhang, Lei Zhang +7Maximum Marginal Likelihood Estimation Robot Imitation Learning
TAMS (Technical Aspects of Multimodal Systems), Department of Informatics, University of Hamburg, Hamburg, Germany. · Technical University of Munich, Germany. · Agile Robots SE, Munich, Germany.
Momentum SVGD-EM for Accelerated Maximum Marginal Likelihood Estimation
Mar 9, 2026Adam Rozzio, Rafael Athanasiades, O. Deniz AkyildizMaximum Marginal Likelihood Estimation Expectation-Maximization
Department of Mathematics, Ecole Normale Supérieure Paris-Saclay, France · Department of Mathematics, Imperial College London, UK
Learning Latent Energy-Based Models via Interacting Particle Langevin Dynamics
Oct 14, 2025Joanna Marks, Tim Y. J. Wang, O. Deniz AkyildizParameter Estimation Latent Variable Models
Department of Mathematics, Imperial College London

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Feature Space Adaptation for Effortless Gaussian Process Flows

Thomas Cowperthwaite, Louis Sharrock, Lachlan Astfalck, Henry Moss

University of Cambridge · University College London · University of New South Wales+1

Maximum Marginal Likelihood Estimation Gaussian Processes Bayesian Inference Diffusion Sampling Kernel Methods

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Outside the linear-Gaussian regime, conditional sampling from Gaussian processes (GPs) is challenging. Recent methods such as FlowGP (Moss et al., (2026)) can condition on arbitrary non-linear and non-Gaussian statements, but at considerable cost: an expensive iterative and high-dimensional diffusion that requires hand-specified kernel hyperparameters. In this paper, we alleviate two significant drawbacks of FlowGP by (1) introducing kernel approximations that enable scaling to high-resolution domains and (2) proposing a way to obtain the marginal likelihood by measuring the work needed to steer the diffusion towards conditioning statements. We enable, for the first time, hyperparameter optimisation within FlowGP and demonstrate our approach on probabilistic downscaling from areal summary statistics, PDE solution inference on irregular domains, and recovery of sea level anomaly fields from non-Gaussian satellite observations.