Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Input Tokens: 18,532 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,428 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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, Îēk), but it does not specify concrete hyperparameter values or system-level training settings for an experimental setup. |