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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
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. |