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Bayesian Learning

1663 directly classified papers

Papers per year

Papers

Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior NIPS 2023
Bayesian Posterior Approximation With Stochastic Ensembles CVPR 2023
Probabilistic Debiasing of Scene Graphs CVPR 2023
Mixed-Effect Thompson Sampling AISTATS 2023
Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees JMLR 2023
Reliable and Interpretable Personalized Federated Learning CVPR 2023
Federated Learning via Meta-Variational Dropout NIPS 2023
End-to-End Learning for Stochastic Optimization: A Bayesian Perspective ICML 2023
Interactive Segmentation As Gaussion Process Classification CVPR 2023
Safety Verification of Nonlinear Systems with Bayesian Neural Network Controllers AAAI 2023
Regularization for Shuffled Data Problems via Exponential Family Priors on the Permutation Group AISTATS 2023
Spectral Bayesian Uncertainty for Image Super-Resolution CVPR 2023
Nearest Neighbor Dirichlet Mixtures JMLR 2023
Inverse-Reference Priors for Fisher Regularization of Bayesian Neural Networks AAAI 2023
Bayesian Progressive Deep Topic Model with Knowledge Informed Textual Data Coarsening Process ICML 2023
Jana: Jointly amortized neural approximation of complex Bayesian models UAI 2023
Joint Word and Morpheme Segmentation with Bayesian Non-Parametric Models EACL 2023
Robust and Scalable Gaussian Process Regression and Its Applications CVPR 2023
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework NIPS 2023
On the Statistical Consistency of Risk-Sensitive Bayesian Decision-Making NIPS 2023
Collective Intelligence in Human-AI Teams: A Bayesian Theory of Mind Approach AAAI 2023
Posterior Contraction for Deep Gaussian Process Priors JMLR 2023
Gaussian Processes with Errors in Variables: Theory and Computation JMLR 2023
Bayesian Cross-Modal Alignment Learning for Few-Shot Out-of-Distribution Generalization AAAI 2023
Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations JMLR 2023