Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Exponential Generalization Bounds with Near-Optimal Rates for $L_q$-Stable Algorithms
Authors: Xiaotong Yuan, Ping Li
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | EXPONENTIAL GENERALIZATION BOUNDS WITH NEAR-OPTIMAL RATES FOR Lq-STABLE ALGORITHMS; In Section 2, we first establish in Theorem 1 an Lq-norm inequality for sums of functions of random variables with Lq-norm bounded difference. Then equipped with such a generalpurpose concentration inequality, we prove in Theorem 2 the following Lq-norm generalization bound for Lq-stable learning algorithms for all q >= 2 |
| Researcher Affiliation | Collaboration | Xiao-Tong Yuan School of Intelligence Science and Technology Nanjing University, Suzhou, 215163, China EMAIL Ping Li Linked In Ads 700 Bellevue Way NE, Bellevue, WA 98004, USA EMAIL |
| Pseudocode | Yes | Algorithm 1: Inexact L0-ERM Oracle Input : A training data set S = {Zi}i [N] and the desired sparsity level k. Output: w S,k. |
| Open Source Code | No | The paper does not contain any statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper does not describe empirical experiments using a specific dataset for training, validation, or testing. |
| Dataset Splits | No | The paper does not describe empirical experiments using a specific dataset for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments, therefore no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, therefore no experimental setup details like hyperparameters or training configurations are provided. |