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marginal likelihood

75 papers

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Also known as

ML

Co-occurring keywords

bayesian inference (1907) gaussian process (1200) variational inference (2069) model selection (456) laplace approximation (70) bayesian model selection (22) hyperparameter optimization (406) latent variable model (564) graphical model (864) model evidence (13)

Papers

Determine the Number of States in Hidden Markov Models via Marginal Likelihood JMLR 2025
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes NIPS 2024
Gibbs-Based Information Criteria and the Over-Parameterized Regime AISTATS 2024
Scores for Learning Discrete Causal Graphs with Unobserved Confounders AAAI 2024
Evidence Estimation in Gaussian Graphical Models Using a Telescoping Block Decomposition of the Precision Matrix JMLR 2024
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood NIPS 2024
On Masked Pre-training and the Marginal Likelihood NIPS 2023
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes ICML 2023
Robust Gaussian process regression with the trimmed marginal likelihood UAI 2023
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels ICML 2023
Bayesian Data Selection JMLR 2023
Learning Layer-wise Equivariances Automatically using Gradients NIPS 2023
Approximately Bayes-optimal pseudo-label selection UAI 2023
Variational Open-Domain Question Answering ICML 2023
Score-Based Diffusion meets Annealed Importance Sampling NIPS 2022
Bayesian Model Selection, the Marginal Likelihood, and Generalization ICML 2022
Last Layer Marginal Likelihood for Invariance Learning AISTATS 2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations NIPS 2022
Learning invariant weights in neural networks UAI 2022
A Hybrid Approximation to the Marginal Likelihood AISTATS 2021
You should evaluate your language model on marginal likelihood over tokenisations EMNLP 2021
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding ICML 2021
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning ICML 2021
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise NIPS 2021
Learning Adjustment Sets from Observational and Limited Experimental Data AAAI 2021