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 Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization
Authors: Tesi Xiao, Krishnakumar Balasubramanian, Saeed Ghadimi
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We included a .ipynb file to reproduce the experimental results. |
| Researcher Affiliation | Academia | Tesi Xiao Department of Statistics University of California, Davis EMAIL Krishnakumar Balasubramanian Department of Statistics University of California, Davis EMAIL Saeed Ghadimi Department of Management Sciences University of Waterloo EMAIL |
| Pseudocode | Yes | Algorithm 1 Linearized NASA with Inexact Conditional Gradient Method (Li NASA+ICG) |
| Open Source Code | Yes | We included a .ipynb file to reproduce the experimental results. |
| Open Datasets | No | The paper does not explicitly mention or provide access information for any publicly available datasets used in experiments. It describes theoretical algorithms with stochastic oracles. |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits, as it primarily focuses on theoretical analysis and algorithm design without detailing specific empirical experiments on datasets. |
| Hardware Specification | No | The paper does not provide any specific hardware specifications used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | No | The paper provides theoretical definitions for algorithm parameters (e.g., Ïk, ÎČk), but it does not specify concrete hyperparameter values or system-level training settings for an experimental setup. |