Empirical Bayes

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Overview

Empirical Bayes uses a population of related observations to estimate prior distributions or hyperparameters, then applies Bayesian inference to individual cases. Common approaches include hierarchical models, marginal-likelihood and nonparametric prior estimation, shrinkage estimators, and variational or simulation-based methods for neural and implicit-likelihood models. These methods support prediction and uncertainty estimation in settings such as federated learning, causal representation learning, privacy, calibration, and scientific modeling. A central challenge is balancing useful adaptation and shrinkage against overfitting or misspecification, while checking calibration and robustness beyond predictive accuracy.

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19 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

  1. Flexible Spectral-Normalized Neural Gaussian Process for Dynamic Aperture Prediction

    Sep 8, 2026Yousra El-Bachir, Frederik Van der Veken, Davide di Croce +4Neural Surrogate ModelingUncertainty Quantification

    Swiss Data Science Center, ETH Zurich & EPFL · CERN, Geneva · EPFL, Lausanne; CERN, Geneva

  2. Hierarchical Empirical-Bayes Naive Bayes: Minimax Smoothing and Calibration with AODE Extension

    Aug 11, 2026Nguyen Thai Anh, Truong Viet Vu, Tran Thien Thanh +2Probability CalibrationEmpirical Bayes

    Faculty of Information Technology, Van Lang School of Technology, Van Lang University, Ho Chi Minh City, 70000, Vietnam · Institute of Information Technology and Electrical-Electronics Engineering, Ho Chi Minh City University of Transport, Ho Chi Minh City, 700000, Vietnam

  3. Personalized Federated Learning via Variance-Aware Nonparametric Empirical Bayes

    Aug 10, 2026Jae Ho Chang, Arnab Auddy, Subhadeep PaulEmpirical BayesFederated Learning

    Department of Statistics, The Ohio State University

  4. Simulation-Based Empirical Bayes

    Jul 23, 2026Xinwei Shen, Diana Cai, Cheng Zhang +1Simulation-Based InferenceParameter Estimation

    Department of Statistics University of Washington · Department of Computer Science Cornell University · School of Mathematical Sciences and Center for Statistical Science Peking University +1

  5. Multimodal Empirical Bayes Variational Autoencoders for Joint Longitudinal and Time-to-Event Modeling

    Jul 15, 2026Anders Sjöberg, Nils Olsson, Marcus Baaz +1Empirical BayesSurvival Analysis

    Fraunhofer-Chalmers Centre, Gothenburg, SE-412 88, Sweden · Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, SE-412 96, Sweden

  6. PEBS: Per-rater Empirical-Bayes Shrinkage for RLHF Reward-Model Calibration

    Jun 25, 2026Arnav RajReward ModelingPost-Hoc Calibration

    Department of Computer Science and Engineering, Indian Institute of Technology Delhi, New Delhi, India.

  7. XMSE-Aware Adaptive Empirical Bayes Estimation

    Jun 25, 2026Minghao Chen, Jiale ZhengParameter EstimationEmpirical Bayes

    Tencent Technology, Shenzhen, China · Huawei Noah’s Ark Lab, Shenzhen, China

  8. Enhancing Differentially Private Mechanisms via Empirical Bayes

    Jun 19, 2026Minwoo Kim, Junyong Park, Sungkyu JungEmpirical BayesDifferential Privacy

    Department of Statistics Seoul National University Seoul, 08826, South Korea · Department of Statistics and Institute for Data Innovation in Science Seoul National University Seoul, 08826, South Korea

  9. Recovering Stranded Discrimination in Knowledge Tracing: Per-Item Bias Correction via Empirical-Bayes Shrinkage

    Jun 12, 2026Xiaoran Yan, Cheng Tang, Atsushi ShimadaPost-Hoc CalibrationEmpirical Bayes

    Kyushu University, Fukuoka, Japan

  10. Environment-Robust Representation Learning with Empirical Bayes

    Jun 3, 2026Yuli Slavutsky, Matthew Shen, Bohan Wu +1Latent Variable ModelsEmpirical Bayes

    Department of Statistics Columbia University New York, NY 10027, USA · Departments of Statistics and Computer Science Columbia University New York, NY 10027, USA

  11. Attention as In-Context Empirical Bayes: A Two-Stage View via Particle Dynamics

    May 28, 2026Matthew Smart, Soumya Ganguly, Nilava Metya +2Self-AttentionEmpirical Bayes

    Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA · Department of Mathematics, Rutgers University, Piscataway, NJ, USA · Department of Physics and Astronomy, Rutgers University, Piscataway, NJ, USA +1

  12. Empirical Bayes Conformal Prediction for Vision and Language Models

    May 22, 2026Jiapeng Zeng, Yogesh Prabhu, Zhanpeng Zeng +2VLM EvaluationEmpirical Bayes

    University of Wisconsin–Madison · University of California San Diego · Xiamen University

  13. Empirical Bayes 1-bit matrix completion

    May 10, 2026Takeru MatsudaEmpirical BayesBayesian Inference

    Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo · Statistical Mathematics Unit, RIKEN Center for Brain Science

  14. Overfitting and Generalizing with (PAC) Bayesian Prediction in Noisy Binary Classification

    Mar 23, 2026Xiaohan Zhu, Mesrob I. Ohannessian, Nathan SrebroPAC-Bayesian TheoryBinary Classification

    The University of Chicago · The University of Illinois at Chicago · Toyota Technological Institute at Chicago

  15. Time-adaptive infinite-dimensional Gaussian process regression on manifolds

    Mar 22, 2026MD Ruiz-Medina, AE Madrid, A Torres-Signes +1Empirical BayesBayesian Inference

  16. Multi-Domain Empirical Bayes for Linearly-Mixed Causal Representations

    Mar 19, 2026Bohan Wu, Julius von Kügelgen, David M. BleiCausal Representation LearningEmpirical Bayes

    Department of Statistics, Columbia University, USA · Seminar for Statistics, ETH Zürich, Switzerland

  17. Function-Space Empirical Bayes Regularisation with Student's t Priors

    Feb 25, 2026Pengcheng Hao, Ercan Engin KuruogluBayesian Neural NetworksBayesian Inference

    Institute of Data and Information, Tsinghua Shenzhen International Graduate School, Shenzhen, China

  18. Universal priors: solving empirical Bayes via Bayesian inference and pretraining

    Feb 16, 2026Nick Cannella, Anzo Teh, Yanjun Han +1Synthetic Data PretrainingEmpirical Bayes

    Courant Institute of Mathematical Sciences, NYU, New York City, NY · Department of EECS, MIT, Cambridge, MA · Courant Institute of Mathematical Sciences and Center for Data Science, NYU, New York City, NY

  19. Empirical Bayesian Multi-Bandit Learning

    Oct 30, 2025Xia Jiang, Rong J. B. ZhuContextual BanditsEmpirical Bayes

    Fudan University, Institute of Science and Technology for Brain-inspired Intelligence, Shanghai, People’s Republic of China