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..
Neural Estimation of Submodular Functions with Applications to Differentiable Subset Selection
Authors: Abir De, Soumen Chakrabarti
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on synthetic and real data show that FLEXSUBNET outperforms several baselines. |
| Researcher Affiliation | Academia | Abir De Soumen Chakrabarti Indian Institute of Technology Bombay EMAIL |
| Pseudocode | No | The paper describes the proposed models and methods in detail using mathematical formulations and descriptive text, but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is in https://tinyurl.com/๏ฌexsubnet. Code is included as part of supplemental material. |
| Open Datasets | Yes | We use the Amazon baby registry dataset [30] which contains 17 product categories. ... We generate |V |=104 samples, where we draw the feature vector zs for each sample s V uniformly at random, i.e., zs Unif[0, 1]d with d=10. |
| Dataset Splits | Yes | We sample |V |=10000 (set,value) instances as described above and split them into train, dev and test folds of equal size. ... We split S into equal-sized training (Strain), dev (Sdev) and test (Stest) folds. |
| Hardware Specification | No | The paper states, "Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See Appendix F in supplemental material." However, Appendix F is not provided in the main paper, so specific hardware details cannot be extracted. |
| Software Dependencies | No | The paper mentions software like BERT and Gumbel-Sinkhorn network but does not provide specific version numbers for these or any other software dependencies used in the experiments. |
| Experiment Setup | Yes | Appendix D provides hyperparameter tuning details for all methods. ... Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Section 5, Appendix D and F in supplemental material. |