Cover Image

On the Convergence of the ELBO to Entropy Sums

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lücke, Jörg, Warnken, Jan
Format:	Preprint
Published:	2022
Subjects:	Machine Learning Information Theory Probability Statistics Theory 65C20, 68T07, 60-08, 62-08, 62F99, 68T05 G.3
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Generative Models with ELBOs Converging to Entropy Sums
by: Warnken, Jan, et al.
Published: (2024)

Convergence rates of non-stationary and deep Gaussian process regression
by: Osborne, Conor, et al.
Published: (2023)

Model-free filtering in high dimensions via projection and score-based diffusions
by: Christensen, Sören, et al.
Published: (2025)

A debiased Bernoulli factory and unbiased estimation of a probability
by: Koskela, Jere, et al.
Published: (2025)

Realizable Bayes-Consistency for General Metric Losses
by: Cohen, Dan Tsir, et al.
Published: (2026)

A PAC-Bayes oracle inequality for sparse neural networks
by: Steffen, Maximilian F., et al.
Published: (2022)

Adaptive thresholding for wavelet-based nonparametric heteroskedastic variance estimation on the sphere
by: Durastanti, Claudio, et al.
Published: (2026)

A Robbins--Monro Sequence That Can Exploit Prior Information For Faster Convergence
by: Liu, Siwei, et al.
Published: (2024)

Optimal Federated Learning for Functional Mean Estimation under Heterogeneous Privacy Constraints
by: Cai, Tony, et al.
Published: (2024)

Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints
by: Cai, T. Tony, et al.
Published: (2024)

Efficient and Minimax Optimal In-context Nonparametric Regression with Transformers
by: Ching, Michelle, et al.
Published: (2026)

Optimal In-context Adaptivity and Distributional Robustness of Transformers
by: Ma, Tianyi, et al.
Published: (2025)

Importance sampling for Sobol' indices estimation
by: Boucharif, Haythem, et al.
Published: (2025)

Least squares approximations in linear statistical inverse learning problems
by: Helin, Tapio
Published: (2022)

Minimax rates for learning kernels in operators
by: Zhang, Sichong, et al.
Published: (2025)

Ranking Perspective for Tree-based Methods with Applications to Symbolic Feature Selection
by: Luo, Hengrui, et al.
Published: (2024)

Quantitative Error Bounds for Scaling Limits of Stochastic Iterative Algorithms
by: Wang, Xiaoyu, et al.
Published: (2025)

On micromodes in Bayesian posterior distributions and their implications for MCMC
by: Agrawal, Sanket, et al.
Published: (2026)

A Minimax Theory of Nonparametric Regression Under Covariate Shift
by: Zamolodtchikov, Petr
Published: (2026)

Computing conservative probabilities of rare events with surrogates
by: Bousquet, Nicolas
Published: (2024)

Estimating Unbounded Density Ratios: Applications in Error Control under Covariate Shift
by: Xu, Shuntuo, et al.
Published: (2025)

Wasserstein Distributionally Robust Nonparametric Regression
by: Liu, Changyu, et al.
Published: (2025)

TILT: Target-induced loss tilting under covariate shift
by: Yamamoto, Kakei, et al.
Published: (2026)

Generalization Error of GAN from the Discriminator's Perspective
by: Yang, Hongkang, et al.
Published: (2021)

Quantile Additive Trend Filtering
by: Zhang, Zhi, et al.
Published: (2023)

New Risk Bounds for 2D Total Variation Denoising
by: Chatterjee, Sabyasachi, et al.
Published: (2019)

Is model selection possible for the $\ell_p$-loss? PCO estimation for regression models
by: Lacour, Claire, et al.
Published: (2025)

Optimal minimax rate of learning nonlocal interaction kernels
by: Wang, Xiong, et al.
Published: (2023)

Spatio-temporal probabilistic forecast using MMAF-guided learning
by: Bardi, Leonardo, et al.
Published: (2026)

Unbalanced Kantorovich-Rubinstein distance, plan, and barycenter on finite spaces: A statistical perspective
by: Hundrieser, Shayan, et al.
Published: (2022)

Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data
by: Gyger, Tim, et al.
Published: (2024)

From Conditional to Unconditional Independence: Testing Conditional Independence via Transport Maps
by: He, Chenxuan, et al.
Published: (2025)

On the Asymptotics of Importance Weighted Variational Inference
by: Cherief-Abdellatif, Badr-Eddine, et al.
Published: (2025)

Universal Shuffle Asymptotics, Part II: Non-Gaussian Limits for Shuffle Privacy -- Poisson, Skellam, and Compound-Poisson Regimes
by: Shvets, Alex
Published: (2026)

Error analysis for learning fractional stochastic differential equations with applications in neural approximations
by: Dehshiri, Mahdi, et al.
Published: (2026)

The Cost of Adaptation under Differential Privacy: Optimal Adaptive Federated Density Estimation
by: Cai, T. Tony, et al.
Published: (2025)

Optimal Rate of Kernel Regression in Large Dimensions
by: Lu, Weihao, et al.
Published: (2023)

Nonparametric velocity estimation in stochastic convection-diffusion equations from multiple local measurements
by: Strauch, Claudia, et al.
Published: (2024)

Estimation of the invariant measure of a multidimensional diffusion from noisy observations
by: Maillet, Raphaël, et al.
Published: (2024)

Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility
by: Lee, Hoil, et al.
Published: (2022)