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..
Discrete-Convex-Analysis-Based Framework for Warm-Starting Algorithms with Predictions
Authors: Shinsaku Sakaue, Taihei Oki
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | As for the practical aspect, experiments in Appendix F show that our DCA-based approach performs comparably to (or slightly better than) the method of [17]. |
| Researcher Affiliation | Academia | Shinsaku Sakaue The University of Tokyo Tokyo, Japan EMAIL Taihei Oki The University of Tokyo Tokyo, Japan EMAIL |
| Pseudocode | Yes | Algorithm 1 Steepest descent method for L-convex (L -convex) function minimization |
| Open Source Code | No | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] All details are described in Appendix F. |
| Open Datasets | No | The instances are generated as follows. We set n = 100 and m = 1000... Edge weights w are randomly sampled from {1, . . . , 100}. (from Appendix F.1). We randomly generate instances of rank-r matroids on a ground set of size n, where elements have weights drawn from {1, . . . , 100}. (from Appendix F.2). We generate image-like data... (from Appendix F.3). No concrete access information for a public dataset is provided. |
| Dataset Splits | No | The paper mentions training on 1000 instances and evaluating on another 1000 instances, implying a train/test split, but does not explicitly define or mention a separate validation set or specific split percentages for validation. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or memory) are provided for the experimental setup. |
| Software Dependencies | No | The paper does not provide specific software or library names with version numbers. |
| Experiment Setup | Yes | We set n = 100 and m = 1000 and connect edges randomly with probability 0.1. (Appendix F.1). We train the prediction ˆp using the online gradient descent on 1000 instances with learning rate = 0.1. (Appendix F.1). We use the problem from [28, Section 3.1] with ϕi(pi) = (pi ci)2 and ψij(pj pi) = λ|pj pi|. We generate image-like data of size 100 100 pixels (n = 10000), where ci is sampled uniformly from {0, . . . , 255}, and set λ = 10. (Appendix F.3). |