Gibbs Measures

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Period ending 2026-09-14

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33 papers

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  1. Thin-shell stability of Gaussian cooling: logconcave sampling with sesteric complexity from a cold start

    Sep 14, 2026Yunbum Kook, Santosh S. VempalaGibbs MeasuresThompson Sampling

  2. Particle GFlowNets: Rethinking Generative Marginalization Models

    Sep 11, 2026Tiago da Silva, Diego Mesquita, Salem LahlouGenerative Flow NetworksGibbs Measures

  3. Conditioning Tree-Based Diffusions and Flows for Probabilistic Tabular Regression

    Jul 30, 2026Silas KoemenGradient Boosted TreeRegression

  4. Accelerated Random-Sweep Gibbs Sampling for Gaussian Graphical Models via Dual Normal Factor Graphs

    Jul 30, 2026Borna Khodabandeh, Mehdi MolkaraieGibbs MeasuresFactor Graphs

  5. The Tractability Landscape of Sampling with Inexact Scores

    Jul 21, 2026Anming Gu, Kevin Tian, Hubert Yang +1Adaptive SamplingGibbs Measures

  6. BMFA: Boundary-Minority Free-Energy Adaptive Screening

    Jul 20, 2026Wenyan Xu, Alizer WongVision TransformerAdaptive Feature Suppression Weights

  7. Connected by Construction: Learning Tractable Near-Tour Marginals for Traveling Salesman Problems

    Jul 13, 2026Ke Sun, Xinyuan Zhang, Xinwu QianSalesman ProblemHamiltonian

  8. Nonparametric Bayesian Inverse Reinforcement Learning with Data-Parallel Gibbs Sampling

    Jul 10, 2026Sai Anirudh Katupilla, Shreeya Dasa LakshminathReward FunctionsGibbs Measures

  9. Learning \mathsf{AC}^0 under Locally Sampleable Graphical Models

    Jul 9, 2026Weiming Feng, Xiongxin Yang, Yixiao Yu +1Optimal Sample ComplexityGibbs Measures

  10. Data-Driven Energy-Based Learning via Gibbs Measures on Hierarchical Structures

    Jun 29, 2026L. U. Abdullaev, F. Herrera, U. A. Rozikov +1Gibbs MeasuresData-Driven

  11. Global Convergence of Gradient Descent for Score Matching in Gaussian Mixtures via Reverse Fisher Divergence

    Jun 18, 2026Alexander TyurinGaussian Mixture ModelsScore-Based Diffusion Model

  12. A Tool for the Synthesis of Adaptive Probabilistic Processors Based on the Ising Model

    Jun 17, 2026Jonathan Juracy Carneiro da Silva, Leonardo R. Gobatto, Jose Rodrigo AzambujaTwo-Dimensional Ising ModelQuantum Annealing

  13. Constrained Diffusion Models with Primal-Dual Inference

    Jun 15, 2026Samar Hadou, Yigit Berkay Uslu, Alejandro RibeiroDiffusion SamplingPrimal-Dual Methods

  14. TreeGRNG: Binary Tree Gaussian Random Number Generator for Efficient Probabilistic AI Hardware

    Jun 15, 2026Jonas Crols, Guilherme Paim, Shirui Zhao +1Bayesian Neural NetworksRandom

  15. Conformal Candidate Certification for Offline Model-Based Optimization

    Jun 13, 2026Seungjin ChoiConformal SelectionConformal Prediction

  16. A unified complexity bound for logconcave sampling

    Jun 10, 2026Yunbum Kook, Santosh S. VempalaGibbs MeasuresOptimization Convergence Analysis

  17. Fixed-Parameter Tractability of Private Synthetic Data Generation

    Jun 9, 2026Badih Ghazi, Cristóbal Guzmán, Pritish Kamath +3Differential PrivacySynthetic Data

  18. Regularized Large Neighborhood Search

    Jun 1, 2026Germain Vivier-Ardisson, Laurent Demonet, Axel Parmentier +1Parallel Continuous Local SearchNeural Solvers

  19. Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo

    May 29, 2026Xiaoyu Wang, Jonathan H. HugginsVariational InferenceGibbs Measures

  20. Blocked Gibbs meets Diffusion Transformers: Unsupervised Learning for Constraint Optimization

    May 24, 2026Yudong W. Xu, Wenhao Li, Xiaoyu Wang +2Constraint SatisfactionDiffusion Transformers

  21. AI4BayesCode: From Natural Language Descriptions to Validated Modular Stateful Bayesian Samplers

    May 18, 2026Jungang Zou, Alex Ziyu Jiang, Qixuan ChenMonte Carlo SamplingBayesian Inference Approaches

  22. Canonical Regularisation of Wide Feature-Learning Neural Networks

    May 18, 2026George Whittle, Pranav Vaidhyanathan, Juliusz Ziomek +2Feature LearningRegularization Methods

  23. Anytime PAC-Bayes for Constrained Density-Ratio Networks under Covariate Shift

    May 17, 2026Paulo Akira F. Enabe, Rodrigo ProvasiCovariate ShiftDistribution Shifts

  24. A proximal gradient algorithm for composite log-concave sampling

    May 12, 2026Linghai Liu, Sinho ChewiOptimal Sample ComplexityGibbs Measures

  25. h-control: Training-Free Camera Control via Block-Conditional Gibbs Refinement

    May 12, 2026Yuzhu Wang, Xi Ye, Duo Su +2Camera ControlCinematic Video Generation

  26. Composing diffusion priors with explicit physical context via generative Gibbs sampling

    May 11, 2026Weizhou Wang, Jonathan Weare, Aaron R. DinnerGibbs MeasuresDiffusion Priors

  27. Decentralized Proximal Stochastic Gradient Langevin Dynamics

    May 1, 2026Mohammad Rafiqul Islam, Lingjiong ZhuMonte Carlo SamplingGibbs Measures

  28. Decentralized Machine Learning with Centralized Performance Guarantees via Gibbs Algorithms

    Apr 22, 2026Yaiza Bermudez, Samir M. Perlaza, Iñaki EsnaolaKullback-Leibler RegularizationGibbs Measures

  29. A Gibbs posterior sampler for inverse problem based on prior diffusion model

    Feb 11, 2026Jean-François GiovannelliPosterior SamplingGibbs Measures

  30. Quenched large deviations for Monte Carlo integration with Coulomb gases

    Aug 2, 2025Martin Rouault, Rémi Bardenet, Mylène MaïdaGibbs MeasuresMonte Carlo

  31. Gibbs randomness-compression proposition

    May 29, 2025M. SüzenGibbs MeasuresInformation-Theoretic Limits

  32. The Method of Gaps: Exact Expressions for the Generalization Error of Supervised Learning Algorithms

    Nov 18, 2024Samir M. Perlaza, Xinying ZouGeneralization BoundsEmpirical Risk Minimization

  33. Monte Carlo with kernel-based Gibbs measures: Guarantees for probabilistic herding

    Feb 18, 2024Martin Rouault, Rémi Bardenet, Mylène MaïdaGibbs MeasuresMonte Carlo