Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Discrete and Continuous Difference of Submodular Minimization
Authors: George Orfanides, Tim Hoheisel, Marwa El Halabi
ICML 2025 | Venue PDF | LLM Run Details | Input Tokens: 38,643 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,863 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |