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..
Optimal approximation for unconstrained non-submodular minimization
Authors: Marwa El Halabi, Stefanie Jegelka
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate our results on noisy submodular minimization and structured sparse learning. In particular, we address the following questions: (1) How robust are different submodular minimization algorithms, including PGM, to multiplicative noise? (2) How well can PGM minimize a non-submodular objective? Do the parameters (α, β) accurately characterize its performance? |
| Researcher Affiliation | Academia | 1Massachusetts Institute of Technology. |
| Pseudocode | No | The paper describes the algorithms and methods used (e.g., PGM), but it does not include a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | All experiments were implemented in Matlab, and con ducted on cluster nodes with 16 Intel Xeon E5 CPU cores and 64 GB RAM. Source code is available at https: //github.com/marwash25/non-sub-min. |
| Open Datasets | Yes | We stopped each algorithm after 1000 iterations for the first dataset and after 400 iterations for the second one, or until the approximate duality gap reached 10 8 . To compute the optimal value H(S ), we use MNP with the noise-free oracle H. We refer the reader to (Bach, 2013, Sect. 12.1) for more details about the algorithms and datasets. |
| Dataset Splits | No | The paper discusses running algorithms for a certain number of iterations or until a duality gap is reached, and averaging results over multiple runs, but it does not specify explicit training, validation, or test dataset splits. For example, it says: 'We stopped each algorithm after 1000 iterations for the first dataset and after 400 iterations for the second one, or until the approximate duality gap reached 10 8'. |
| Hardware Specification | Yes | All experiments were implemented in Matlab, and con ducted on cluster nodes with 16 Intel Xeon E5 CPU cores and 64 GB RAM. |
| Software Dependencies | No | The paper states, 'All experiments were implemented in Matlab', but it does not provide a specific version number for Matlab or any other software libraries or dependencies used. |
| Experiment Setup | Yes | We stopped each algorithm after 1000 iterations for the first dataset and after 400 iterations for the second one, or until the approximate duality gap reached 10 8. We set d = 250, k = 20 and vary the number of measurements n between d/4 and 2d. We compare the solutions obtained by minimizing the least squares loss (x) = ky Axk2 with 2 2 the three regularizers: The range function F r, where H is optimized via exhaustive search (OPT-Range), or via PGM (PGM-Range);... λ was varied between 10 4 and 10. Results are averaged over 5 runs. |