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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
How to address monotonicity for model risk management?
Authors: Dangxing Chen, Weicheng Ye
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 19,986 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 1,953 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 | As a result of empirical examples, we demonstrate that monotonicity is often violated in practice and that monotonic groves of neural additive models are transparent, accountable, and fair. |
| Researcher Affiliation | Academia | 1Zu Chongzhi Center for Mathematics and Computational Sciences, Duke Kunshan University, Kunshan, Jiangsu, China. Correspondence to: Dangxing Chen <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Monotonic Groves of Neural Additive Model |
| Open Source Code | Yes | The code is built and modified based on (Tshitoyan, 2023). |
| Open Datasets | Yes | A popularly used dataset is the Kaggle credit score dataset 1. ... A report published by Pro Publica in 2016 provided recidivism data for defendants in Broward County, Florida (Pro, 2016). ... This dataset (Ahmad et al., 2017; Chicco & Jurman, 2020) contains the medical records of 299 patients who had heart failure... |
| Dataset Splits | No | For all our experiments, the dataset is randomly partitioned into 75% training and 25% test sets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments. |
| Software Dependencies | No | The paper mentions 'The code is built and modified based on (Tshitoyan, 2023).' but does not specify software versions (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For all our experiments, the dataset is randomly partitioned into 75% training and 25% test sets. All neural networks contain 1 hidden layer with 2 units, logistic activation, and no regulation. |