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..
Curriculum learning for multilevel budgeted combinatorial problems
Authors: Adel Nabli, Margarida Carvalho
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report results close to optimality on graphs up to 100 nodes and a 185 speedup on average compared to the quickest exact solver known for the Multilevel Critical Node problem, a max-min-max trilevel problem that has been shown to be at least Σp 2-hard. ... Section 6 Computational Results |
| Researcher Affiliation | Academia | Adel Nabli Margarida Carvalho CIRRELT and Département d Informatique et de Recherche Opérationnelle Université de Montréal EMAIL EMAIL |
| Pseudocode | Yes | See Appendix B.1 for the pseudo-code. ... Algorithm 1: Multi L-Cur ... The pseudo-code for the Greedy Rollout function is available in Appendix B.2 ... Its pseudo-code is available in Appendix B.3. |
| Open Source Code | Yes | We make our code publicly available: https://github.com/Adel Nabli/MCN |
| Open Datasets | Yes | MCN [1] along with a publicly available dataset of solved instances2, which we can use to assess the quality of our heuristic. ... 2https://github.com/mxmmargarida/Critical-Node-Problem ... The first distribution of instances considered is D(1), constituted of Erdos-Renyi graphs [18] with size |V |(1) [[10, 23]] and arc density d(1) [0.1, 0.2]. ... The second distribution of instances D(2) focused on larger graphs with |V |(2) [[20, 60]], d(2) [0.05, 0.15]. |
| Dataset Splits | No | Algorithm 1 mentions 'Create Dj train, Dj val by sampling' and 'validation set' (Lj val) but does not provide specific details on the split percentages, absolute counts, or detailed splitting methodology. |
| Hardware Specification | Yes | To train our agent and at inference, we used one gpu of a cluster of NVidia V100SXM2 with 16G of memory4. |
| Software Dependencies | Yes | The architectures presented in Figure 2 was implemented with Pytorch Geometric [21] and Pytorch 1.4 [50]. |
| Experiment Setup | No | The paper mentions using 'Adam [35]' as the optimizer but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed optimizer settings in the main text. It refers to Appendix D for 'Further details of the implementation', but such details are not explicitly present in the main body. |