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 [1].
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. |