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..
A Lower Bound for the Optimization of Finite Sums
Authors: Alekh Agarwal, Leon Bottou
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper presents a lower bound for optimizing a finite sum of n functions, where each function is L-smooth and the sum is µ-strongly convex. |
| Researcher Affiliation | Industry | Alekh Agarwal EMAIL Microsoft Research NYC, New York, NY. Léon Bottou EMAIL Facebook AI Research, New York, NY. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper focused on lower bounds for optimization. It does not conduct experiments with datasets, and therefore no information about publicly available training data is provided. |
| Dataset Splits | No | This is a theoretical paper focused on lower bounds for optimization. It does not conduct experiments with datasets, and therefore no information about validation dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments that would require software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or system-level training settings. |