posterior inference

Learning Temporal Evolution of Spatial Dependence with Generalized Spatiotemporal Gaussian Process Models JMLR 2022 Learning Tractable Probabilistic Models from Inconsistent Local Estimates NeurIPS 2022 On the detrimental effect of invariances in the likelihood for variational inference NeurIPS 2022 Probabilistic surrogate networks for simulators with unbounded randomness UAI 2022 Sampling with Mirrored Stein Operators ICLR 2022 Scale Mixtures of Neural Network Gaussian Processes ICLR 2022 Tractable Uncertainty for Structure Learning ICML 2022 Bayesian Bellman Operators NeurIPS 2021 Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective AISTATS 2021 Bayesian time-aligned factor analysis of paired multivariate time series JMLR 2021 Differentially Private Bayesian Inference for Generalized Linear Models ICML 2021 Doubly non-central beta matrix factorization for DNA methylation data UAI 2021 Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms ICLR 2021 Improving predictions of Bayesian neural nets via local linearization AISTATS 2021 Leveraging probabilistic circuits for nonparametric multi-output regression UAI 2021 Nonparametric Hamiltonian Monte Carlo ICML 2021 Particle-Gibbs Sampling for Bayesian Feature Allocation Models JMLR 2021 RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs ICLR 2021 Variational Multi-Task Learning with Gumbel-Softmax Priors NeurIPS 2021 Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks NeurIPS 2020 Black-Box Inference for Non-Linear Latent Force Models AISTATS 2020 Deep Structured Mixtures of Gaussian Processes AISTATS 2020 Differentiable Algorithm for Marginalising Changepoints AAAI 2020 HAF-SVG: Hierarchical Stochastic Video Generation with Aligned Features IJCAI 2020 Likelihood-free MCMC with Amortized Approximate Ratio Estimators ICML 2020