empirical baye

Gradient Origin Networks ICLR 2021 Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference AISTATS 2021 On Empirical Bayes Variational Autoencoder: An Excess Risk Bound COLT 2021 Empirical Bayes Transductive Meta-Learning with Synthetic Gradients ICLR 2020 Empirical Priors for Prediction in Sparse High-dimensional Linear Regression JMLR 2020 Extrapolating the profile of a finite population COLT 2020 Gradient-EM Bayesian Meta-Learning NeurIPS 2020 Meta-Learning with Shared Amortized Variational Inference ICML 2020 Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes AAAI 2020 Adversarial Variational Optimization of Non-Differentiable Simulators AISTATS 2019 Covariate-Powered Empirical Bayes Estimation NeurIPS 2019 Deterministic Variational Inference for Robust Bayesian Neural Networks ICLR 2019 Fast Automatic Smoothing for Generalized Additive Models JMLR 2019 Learning Neural Networks with Adaptive Regularization NeurIPS 2019 Neural Empirical Bayes JMLR 2019 Shared Subspace Models for Multi-Group Covariance Estimation JMLR 2019 Black Box FDR ICML 2018 Principled Selection of Hyperparameters in the Latent Dirichlet Allocation Model JMLR 2018 Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior NeurIPS 2018 An Empirical Bayes Approach to Optimizing Machine Learning Algorithms NeurIPS 2017 An Empirical-Bayes Score for Discrete Bayesian Networks PGM 2016 Scalable geometric density estimation AISTATS 2016 Information-based learning by agents in unbounded state spaces NeurIPS 2014 Bayesian entropy estimation for binary spike train data using parametric prior knowledge NeurIPS 2013 Spike train entropy-rate estimation using hierarchical Dirichlet process priors NeurIPS 2013