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..
Uniform Convergence of Gradients for Non-Convex Learning and Optimization
Authors: Dylan J. Foster, Ayush Sekhari, Karthik Sridharan
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The goal of the present work is to introduce learning-theoretic tools to in a general sense improve understanding of when and why gradient-based methods succeed for non-convex learning problems. Our precise technical contributions are as follows: We bring vector-valued Rademacher complexities [30] and associated vector-valued contraction principles to bear on the analysis of uniform convergence for gradients. |
| Researcher Affiliation | Academia | Dylan J. Foster Cornell University EMAIL Ayush Sekhari Cornell University EMAIL Karthik Sridharan Cornell University EMAIL |
| Pseudocode | No | The paper describes a 'meta-algorithm' and references other algorithms, but it does not include any formal pseudocode blocks or algorithm listings with labels such as 'Algorithm 1'. |
| Open Source Code | No | The paper does not contain any statement about releasing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical analysis of learning problems. It does not conduct empirical experiments using specific datasets, and therefore does not provide access information for a publicly available dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments or dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any computational experiments, so there is no mention of specific hardware specifications used for running experiments. |
| Software Dependencies | No | The paper is purely theoretical and does not describe any computational experiments. It does not list specific software dependencies with version numbers that would be required for replication. |
| Experiment Setup | No | The paper is purely theoretical and does not describe any experimental setup details, hyperparameters, or training configurations. |