laplace approximation

Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks ICML 2020 Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond NeurIPS 2020 Dependent relevance determination for smooth and structured sparse regression JMLR 2019 Fast Automatic Smoothing for Generalized Additive Models JMLR 2019 LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations ICML 2019 Towards Zero-shot Language Modeling EMNLP 2019 Variational Laplace Autoencoders ICML 2019 A Scalable Laplace Approximation for Neural Networks ICLR 2018 Graphical model inference: Sequential Monte Carlo meets deterministic approximations NeurIPS 2018 Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting NeurIPS 2018 State Space Gaussian Processes with Non-Gaussian Likelihood ICML 2018 Fast Bayesian Intensity Estimation for the Permanental Process ICML 2017 PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference NeurIPS 2017 Scalable Variational Inference for Super Resolution Microscopy AISTATS 2017 Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models JMLR 2016 Stochastic Neural Networks with Monotonic Activation Functions AISTATS 2016 Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods ICML 2015 Unlocking neural population non-stationarities using hierarchical dynamics models NeurIPS 2015 Latent Gaussian Models for Topic Modeling AISTATS 2014 Low-dimensional models of neural population activity in sensory cortical circuits NeurIPS 2014 Evidence Estimation for Bayesian Partially Observed MRFs AISTATS 2013 Integrated Non-Factorized Variational Inference NeurIPS 2013 Bayesian Nonparametric Modeling of Suicide Attempts NeurIPS 2012 Kernel Topic Models AISTATS 2012 Active learning of neural response functions with Gaussian processes NeurIPS 2011