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..
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. |