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Bayesian Learning

1663 directly classified papers

Papers per year

Papers

On the Statistical Consistency of Risk-Sensitive Bayesian Decision-Making NIPS 2023
Sparse Bayesian optimization AISTATS 2023
Implicit Manifold Gaussian Process Regression NIPS 2023
The Behavior and Convergence of Local Bayesian Optimization NIPS 2023
A Finite-Particle Convergence Rate for Stein Variational Gradient Descent NIPS 2023
Noise-Adaptive Thompson Sampling for Linear Contextual Bandits NIPS 2023
The Memory-Perturbation Equation: Understanding Model's Sensitivity to Data NIPS 2023
Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees JMLR 2023
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework NIPS 2023
An Eigenmodel for Dynamic Multilayer Networks JMLR 2023
Federated Learning via Meta-Variational Dropout NIPS 2023
Unexpected Improvements to Expected Improvement for Bayesian Optimization NIPS 2023
Toward Better PAC-Bayes Bounds for Uniformly Stable Algorithms NIPS 2023
Asymptotic Bayes risk of semi-supervised multitask learning on Gaussian mixture AISTATS 2023
Reliable and Interpretable Personalized Federated Learning CVPR 2023
Gradient-Based Uncertainty Attribution for Explainable Bayesian Deep Learning CVPR 2023
Multilevel Bayesian Quadrature AISTATS 2023
Bayesian Hierarchical Models for Counterfactual Estimation AISTATS 2023
NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge AISTATS 2023
Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior NIPS 2023
On the Sublinear Regret of GP-UCB NIPS 2023
Robust and Scalable Gaussian Process Regression and Its Applications CVPR 2023
Probabilistic Debiasing of Scene Graphs CVPR 2023
Interactive Segmentation As Gaussion Process Classification CVPR 2023
CICL_DMS at SemEval-2023 Task 11: Learning With Disagreements (Le-Wi-Di) SEMEVAL 2023