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Bayesian Inference

5,566 papers

Papers per year

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522
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Papers

On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models NIPS 2024
Sparse Bayesian Generative Modeling for Compressive Sensing NIPS 2024
Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound NIPS 2024
Reparameterization invariance in approximate Bayesian inference NIPS 2024
Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities NIPS 2024
Sample Efficient Bayesian Learning of Causal Graphs from Interventions NIPS 2024
Marginal Causal Flows for Validation and Inference NIPS 2024
Expected Probabilistic Hierarchies NIPS 2024
FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning NIPS 2024
TreeVI: Reparameterizable Tree-structured Variational Inference for Instance-level Correlation Capturing NIPS 2024
Benchmarking LLMs via Uncertainty Quantification NIPS 2024
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes NIPS 2024
Linear Uncertainty Quantification of Graphical Model Inference NIPS 2024
Fixed Confidence Best Arm Identification in the Bayesian Setting NIPS 2024
Recursive PAC-Bayes: A Frequentist Approach to Sequential Prior Updates with No Information Loss NIPS 2024
Inferring stochastic low-rank recurrent neural networks from neural data NIPS 2024
Globally Convergent Variational Inference NIPS 2024
Approximation-Aware Bayesian Optimization NIPS 2024
Sketched Lanczos uncertainty score: a low-memory summary of the Fisher information NIPS 2024
A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems NIPS 2024
PAC-Bayes-Chernoff bounds for unbounded losses NIPS 2024
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood NIPS 2024
Generalization of Hamiltonian algorithms NIPS 2024
Streaming Bayes GFlowNets NIPS 2024
The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof NIPS 2024