A Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization
Authors: Tesi Xiao, Krishnakumar Balasubramanian, Saeed Ghadimi
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 texiao@ucdavis.edu Krishnakumar Balasubramanian Department of Statistics University of California, Davis kbala@ucdavis.edu Saeed Ghadimi Department of Management Sciences University of Waterloo sghadimi@uwaterloo.ca |
| 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., τk, βk), but it does not specify concrete hyperparameter values or system-level training settings for an experimental setup. |