Convergence Rates of Variational Inference in Sparse Deep Learning
Authors: Badr-Eddine Chérief-Abdellatif
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we show that variational inference for sparse deep learning retains precisely the same generalization properties than exact Bayesian inference. In particular, we show that a wise choice of the neural network architecture leads to near-minimax rates of convergence for Hölder smooth functions. Additionally, we show that the model selection framework over the architecture of the network via ELBO maximization does not overfit and adaptively achieves the optimal rate of convergence. |
| Researcher Affiliation | Academia | CREST ENSAE Institut Polytechnique de Paris. Correspondence to: Badr-Eddine Chérief-Abdellatif <badr.eddine.cherief.abdellatif@ensae.fr> |
| Pseudocode | No | The paper describes methods and theoretical analyses but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for the methodology described. |
| Open Datasets | No | The paper describes a theoretical nonparametric regression framework using a collection of random variables, but it does not use or provide access information for a specific, publicly available dataset. |
| Dataset Splits | No | The paper is purely theoretical and does not describe empirical experiments with data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not report on computational experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe specific software implementations with version numbers. |
| Experiment Setup | No | The paper is purely theoretical and does not describe an experimental setup with specific hyperparameters or system-level training settings. |