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].
Discrete and Continuous Difference of Submodular Minimization
Authors: George Orfanides, Tim Hoheisel, Marwa El Halabi
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that our method outperforms baselines in integer compressive sensing and integer least squares. |
| Researcher Affiliation | Collaboration | 1Department of Mathematics and Statistics, Mc Gill University, Montreal 2Samsung AI Lab, Montreal. |
| Pseudocode | Yes | Algorithm 1 DCA with local search |
| Open Source Code | Yes | The code is available at https://github.com/SamsungSAILMontreal/cont-diffsubmin. |
| Open Datasets | No | We sample x uniformly from X = {−1, 0, 2, 3}n with n = 100, draw the entries of A i.i.d from N(0, 1), and vary m from n to 2n. ... We set X = {−1, 0, 1}n, n = 256, s = 26 = 0.1n, and draw A i.i.d from N(0, 1/m), with m varied from 26 to n. |
| Dataset Splits | No | The paper describes generating synthetic data for integer least squares and integer compressed sensing problems, but does not refer to pre-existing datasets nor specify any training/test/validation splits for such datasets. It defines parameters for data generation and evaluation. |
| Hardware Specification | No | The paper mentions that 'All methods were implemented in MATLAB' but does not specify any particular hardware (e.g., CPU, GPU models) used for running the experiments. |
| Software Dependencies | Yes | We obtain an optimal solution x∗ using Gurobi 10.0.1 (Gurobi Optimization, LLC, 2024). |
| Experiment Setup | Yes | We sample x uniformly from X = {−1, 0, 2, 3}n with n = 100, draw the entries of A i.i.d from N(0, 1), and vary m from n to 2n. The noise variance σ2 is set to achieve a target signal-to-noise ratio (SNRdB) of 20 dB. ... In DCA-LS, we use a maximum of T = 50 outer iterations and set ϵ = 10−5. |