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Found 6 papers, 5 papers with code
An Ordering of Divergences for Variational Inference with Factorized Gaussian Approximations
Our analysis covers the KL divergence, the R\'enyi divergences, and a score-based divergence that compares $\nabla\log p$ and $\nabla\log q$.
Listening to the Noise: Blind Denoising with Gibbs Diffusion
Assuming arbitrary parametric Gaussian noise, we develop a Gibbs algorithm that alternates sampling steps from a conditional diffusion model trained to map the signal prior to the family of noise distributions, and a Monte Carlo sampler to infer the noise parameters.
Batch and match: black-box variational inference with a score-based divergence
We analyze the convergence of BaM when the target distribution is Gaussian, and we prove that in the limit of infinite batch size the variational parameter updates converge exponentially quickly to the target mean and covariance.
Amortized Variational Inference: When and Why?
We then show, on a broader class of models, how to expand the domain of AVI's inference function to improve its solution, and we provide examples, e. g. hidden Markov models, where the amortization gap cannot be closed.
The Shrinkage-Delinkage Trade-off: An Analysis of Factorized Gaussian Approximations for Variational Inference
We study various manifestations of this trade-off, notably one where, as the dimension of the problem grows, the per-component entropy gap between $p$ and $q$ becomes vanishingly small even though $q$ underestimates every componentwise variance by a constant multiplicative factor.
A Review of automatic differentiation and its efficient implementation
Derivatives play a critical role in computational statistics, examples being Bayesian inference using Hamiltonian Monte Carlo sampling and the training of neural networks.
Mathematical Software Computation
