Fast Excess Risk Rates via Offset Rademacher Complexity
Authors: Chenguang Duan, Yuling Jiao, Lican Kang, Xiliang Lu, Jerry Zhijian Yang
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Based on the offset Rademacher complexity, this work outlines a systematical framework for deriving sharp excess risk bounds in statistical learning without Bernstein condition. In addition to recovering fast rates in a unified way for some parametric and nonparametric supervised learning models with minimum identifiability assumptions, we also obtain new and improved results for LAD (sparse) linear regression and deep logistic regression with deep Re LU neural networks, respectively. |
| Researcher Affiliation | Academia | 1School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, P.R. China. 2Hubei National Center for Applied Mathematics, Wuhan University, Wuhan, 430072, P.R. China. 3Hubei Key Laboratory of Computational Science, Wuhan University, Wuhan, 430072, P.R. China. 4Center for Quantitative Medicine, Duke-NUS Medical School, Singapore. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not report on experiments using specific, publicly available datasets. It refers to a generic 'sample D := {(Xi, Yi)}n i=1 drawn from ยต' as a theoretical construct. |
| Dataset Splits | No | The paper is theoretical and does not describe any specific training, validation, or test dataset splits. The term 'validation' in the paper refers to validating theoretical results, not data splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide details about an experimental setup, hyperparameters, or training configurations. |