laplace approximation

Diffusion Posterior Sampling for General Noisy Inverse Problems ICLR 2023 Diversity-enhanced probabilistic ensemble for uncertainty estimation UAI 2023 Effective Bayesian Heteroscedastic Regression with Deep Neural Networks NeurIPS 2023 GEX: A flexible method for approximating influence via Geometric Ensemble NeurIPS 2023 Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models ICML 2023 Learning Layer-wise Equivariances Automatically using Gradients NeurIPS 2023 Riemannian Laplace approximations for Bayesian neural networks NeurIPS 2023 Sampling-based inference for large linear models, with application to linearised Laplace ICLR 2023 Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel ICLR 2023 Sparse Spectral Bayesian Permanental Process with Generalized Kernel AISTATS 2023 Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels ICML 2023 The Fine Print on Tempered Posteriors ACML 2023 Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations NeurIPS 2022 Laplace approximated Gaussian process state-space models UAI 2022 Laplace Approximation Based Epistemic Uncertainty Estimation in 3D Object Detection CoRL 2022 Laplacian Autoencoders for Learning Stochastic Representations NeurIPS 2022 Merging Models with Fisher-Weighted Averaging NeurIPS 2022 Addressing Catastrophic Forgetting in Few-Shot Problems ICML 2021 Asymptotic Normality, Concentration, and Coverage of Generalized Posteriors JMLR 2021 Bayesian Deep Learning via Subnetwork Inference ICML 2021 Differentially Private Regression and Classification with Sparse Gaussian Processes JMLR 2021 Improving predictions of Bayesian neural nets via local linearization AISTATS 2021 Laplace Redux - Effortless Bayesian Deep Learning NeurIPS 2021 Learnable uncertainty under Laplace approximations UAI 2021 Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning ICML 2021