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Bayesian Inference

1724 directly classified papers

Papers per year

Papers

A Framework for Improving the Reliability of Black-box Variational Inference JMLR 2024
Tensor-train methods for sequential state and parameter learning in state-space models JMLR 2024
Flexible Bayesian Product Mixture Models for Vector Autoregressions JMLR 2024
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization JMLR 2024
On the Computational Complexity of Metropolis-Adjusted Langevin Algorithms for Bayesian Posterior Sampling JMLR 2024
PAC-Bayes-Chernoff bounds for unbounded losses NIPS 2024
Globally Convergent Variational Inference NIPS 2024
MAP- and MLE-Based Teaching JMLR 2024
Nonparametric Copula Models for Multivariate, Mixed, and Missing Data JMLR 2024
On Unbiased Estimation for Partially Observed Diffusions JMLR 2024
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees JMLR 2024
Differentially private methods for managing model uncertainty in linear regression JMLR 2024
Robust Gaussian Processes via Relevance Pursuit NIPS 2024
Improved Bayes Regret Bounds for Multi-Task Hierarchical Bayesian Bandit Algorithms NIPS 2024
Prior-dependent analysis of posterior sampling reinforcement learning with function approximation AISTATS 2024
Spatial meshing for general Bayesian multivariate models JMLR 2024
Bayesian Regression Markets JMLR 2024
Penalized Overdamped and Underdamped Langevin Monte Carlo Algorithms for Constrained Sampling JMLR 2024
autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm AISTATS 2024
Precise Model Benchmarking with Only a Few Observations EMNLP 2024
Bayesian Calibration of Win Rate Estimation with LLM Evaluators EMNLP 2024
Sample Efficient Bayesian Learning of Causal Graphs from Interventions NIPS 2024
Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs AISTATS 2024
Causal vs. Anticausal merging of predictors NIPS 2024
Simulation-Based Stacking AISTATS 2024