empirical baye

An Empirical Bayes Perspective on Heteroskedastic Mean Estimation COLT 2026 Label-Wise uncertainty decomposition for Multi-label Classification by Maximizing Type II Likelihood UAI 2026 Universal priors: solving empirical Bayes via Bayesian inference and pretraining COLT 2026 Prediction-Powered Adaptive Shrinkage Estimation ICML 2025 A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression JMLR 2024 Chain of Log-Concave Markov Chains ICLR 2024 CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training NeurIPS 2024 Improving Neural Additive Models with Bayesian Principles ICML 2024 On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models JMLR 2024 On Truthing Issues in Supervised Classification JMLR 2024 Optimal score estimation via empirical Bayes smoothing COLT 2024 Precise Model Benchmarking with Only a Few Observations EMNLP 2024 ReBandit: Random Effects Based Online RL Algorithm for Reducing Cannabis Use IJCAI 2024 Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity NeurIPS 2024 An Empirical Bayes Framework for Open-Domain Dialogue Generation EMNLP 2023 A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy ICLR 2023 Empirical Bayes via ERM and Rademacher complexities: the Poisson model COLT 2023 Function-Space Regularization in Neural Networks: A Probabilistic Perspective ICML 2023 Empirical bayes approach to truth discovery problems UAI 2022 Multimeasurement Generative Models ICLR 2022 Multiple Testing in Nonparametric Hidden Markov Models: An Empirical Bayes Approach JMLR 2022 A General Framework for Empirical Bayes Estimation in Discrete Linear Exponential Family JMLR 2021 Empirical Bayes Matrix Factorization JMLR 2021 Flexible Signal Denoising via Flexible Empirical Bayes Shrinkage JMLR 2021 For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets NeurIPS 2021