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].
CCCP is Frank-Wolfe in disguise
Authors: Alp Yurtsever, Suvrit Sra
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper uncovers a simple but rather surprising connection: it shows that the wellknown convex-concave procedure (CCCP) and its generalization to constrained problems are both special cases of the Frank-Wolfe (FW) method. This connection not only provides insight of deep (in our opinion) pedagogical value, but also transfers the recently discovered convergence theory of nonconvex Frank-Wolfe methods immediately to CCCP, closing a long-standing gap in its non-asymptotic convergence theory. We hope the viewpoint uncovered by this paper spurs the transfer of other advances made for FW to both CCCP and its generalizations. |
| Researcher Affiliation | Academia | Alp Yurtsever Umeå University EMAIL Suvrit Sra Massachusetts Institute of Technology EMAIL |
| Pseudocode | No | The paper describes the steps of the Frank-Wolfe algorithm and its variants (FW, FW+) within the text, using symbolic representations like (FW) and (FW+), but it does not present a formally labeled "Pseudocode" or "Algorithm" block. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. The ethics review guidelines explicitly state 'N/A' for experiments, implying no code for experimental reproduction. |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies with datasets. Therefore, it does not mention specific datasets or their public availability. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical studies with dataset splits. Therefore, it does not provide details on training/validation/test splits. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments. Therefore, it does not specify any hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments or provide implementation details that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not report on experiments. Therefore, it does not include details on experimental setup such as hyperparameters or training settings. |