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 [1].
Towards Problem-dependent Optimal Learning Rates
Authors: Yunbei Xu, Assaf Zeevi
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study problem-dependent rates, i.e., generalization errors that scale tightly with the variance or the effective loss at the "best hypothesis." In this paper we propose a new framework based on a "uniform localized convergence" principle. We provide the ο¬rst (moment-penalized) estimator that achieves the optimal variance-dependent rate for general "rich" classes; we also establish improved loss-dependent rate for standard empirical risk minimization. |
| Researcher Affiliation | Academia | Yunbei Xu Columbia University New York, NY 10027 EMAIL Assaf Zeevi Columbia University New York, NY 10025 EMAIL |
| Pseudocode | No | The paper describes "Strategy 1" and "Strategy 2" as procedural steps within the main text, but these are not formatted as distinct pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the described methodology or links to code repositories. |
| Open Datasets | No | The paper is theoretical and does not describe experiments or use datasets, so no dataset access information is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments, so no specific dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments, thus no specific software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is theoretical and does not describe experiments, thus no specific experimental setup details like hyperparameters or training configurations are provided. |