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..
GRAND-SLAMIN’ Interpretable Additive Modeling with Structural Constraints
Authors: Shibal Ibrahim, Gabriel Afriat, Kayhan Behdin, Rahul Mazumder
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on real-world datasets show that our toolkit performs favorably in terms of performance, variable selection and scalability when compared with popular toolkits to fit GAMs with interactions. |
| Researcher Affiliation | Academia | Shibal Ibrahim MIT Cambridge, MA EMAIL; Gabriel Isaac Afriat MIT Cambridge, MA EMAIL; Kayhan Behdin MIT Cambridge, MA EMAIL; Rahul Mazumder MIT Cambridge, MA EMAIL |
| Pseudocode | No | The paper describes methodologies but does not include a clearly labeled pseudocode block or algorithm section. |
| Open Source Code | Yes | Our code is available at https://github.com/mazumder-lab/grandslamin. |
| Open Datasets | Yes | We use a collection of 16 open-source classification datasets (binary, multiclass and regression) from various domains... They are from Penn Machine Learning Benchmarks (PMLB) [41] and UCI databases [8]. |
| Dataset Splits | Yes | When no test set was available, we treated the original validation set as the test set and split the training set into 80% training and 20% validation. For remaining, we randomly split each of the dataset into 60% training, 20% validation and 20% testing sets. |
| Hardware Specification | Yes | We used a cluster running Ubuntu 7.5.0 and equipped with Intel Xeon Platinum 8260 CPUs and Nvidia Volta V100 GPUs. For all experiments of Sec. 6, each job involving GRAND-SLAMIN, EBM, Node-GAM, SIAN, GAMI-Net and DNN were run on 8 core, 32GB RAM. Jobs involving larger datasets (p > 100) were run on Tesla V100 GPUs. |
| Software Dependencies | No | The paper mentions Ubuntu 7.5.0 (an operating system) and Optuna [2] for hyperparameter tuning but does not specify version numbers for other core software dependencies (e.g., programming languages, deep learning frameworks, or libraries) required for replicating the methodology. |
| Experiment Setup | Yes | Learning Rates: Discrete uniform in the set {0.05, 0.01, 0.005} for Adam with multi-step decay rate of 0.9 every 25 epochs. Batch-size: Discrete uniform in the set {64, 256}. λ for selection: Discrete uniform in the set of 11 values {0, 1e 6, , 1e 3}. γ for Smooth-step: Discrete uniform in the set {0.01, 0.1, 1}. τ for Entropy Regularization: Discrete uniform in the set {0.001, 0.01, 0.1}. α for relative penalty on interactions: Discrete uniform in the set {1, 10}. Epochs: 1000 with early stopping (patience=50) based on validation loss. |