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..
Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator
Authors: Alp Yurtsever, Suvrit Sra, Volkan Cevher
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our focus in this paper is on the theoretical complexity of stochastic and ο¬nite-sum FW, with an aim to identify and present the tightest results known so far. To this end, we also propose a class of novel variance-reduced stochastic optimization algorithms, based on the recent stochastic pathintegrated differential estimator technique (SPIDER) of Fang et al. (2018). ... Developing a well-tuned implementation, including one that incorporates parallel optimization, is an important piece of future work. |
| Researcher Affiliation | Academia | 1Ecole Polytechnique F ed erale de Lausanne, Switzerland 2Massachusetts Institute of Technology, USA. |
| Pseudocode | Yes | Algorithm 1 Frank-Wolfe algorithm Algorithm 2 SPIDER Frank-Wolfe Algorithm 3 SPIDER Conditional Gradient Sliding |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with specific datasets; therefore, no information about publicly available training datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with specific datasets; therefore, no training/test/validation dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not report on empirical experiments; therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on empirical experiments; therefore, no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not report on empirical experiments; therefore, no experimental setup details like hyperparameters or training settings are provided. |