Constrained Monotonic Neural Networks
Authors: Davor Runje, Sharath M Shankaranarayana
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show this approach of building monotonic neural networks has better accuracy when compared to other state-of-the-art methods, while being the simplest one in the sense of having the least number of parameters, and not requiring any modifications to the learning procedure or post-learning steps. |
| Researcher Affiliation | Collaboration | 1Airt Research, Zagreb, Croatia 2Algebra University College, Zagreb, Croatia. |
| Pseudocode | No | The paper describes the proposed method using mathematical definitions and equations (e.g., Definition 1, Definition 3), but it does not include a discrete pseudocode block or algorithm section. |
| Open Source Code | Yes | The code is publicly available at (Runje & Shankaranarayana, 2023a), while the preprocessed datasets for experiments are available at (Runje & Shankaranarayana, 2023b). |
| Open Datasets | Yes | For the first set of experiments, we use the datasets employed by authors in (Liu et al., 2020) and use the exact train and test split for proper comparison. We perform experiments on 3 datasets: COMPAS (J. Angwin & Kirchner, 2016)... Blog Feedback Regression (Buza, 2014)... Loan Defaulter1... For the second set of experiments, we use 2 datasets: Auto MPG (which is a regression dataset with 3 monotonic features) and Heart Disease... the preprocessed datasets for experiments are available at (Runje & Shankaranarayana, 2023b). |
| Dataset Splits | No | For the first set of experiments, we use the datasets employed by authors in (Liu et al., 2020) and use the exact train and test split for proper comparison. The train-test splits of 80% 20% are used for all comparison experiments. |
| Hardware Specification | Yes | All experiments were performed using a Google Colaboratory instance with NVidia Tesla T4 GPU (Bisong, 2019). |
| Software Dependencies | Yes | The code for experiments was written in the Keras framework (Chollet et al., 2015) and Keras Tuner (O Malley et al., 2019) via integration from the Tensorflow framework, version 2.11 (Abadi et al., 2015). |
| Experiment Setup | No | We employ Bayesian optimization tuning with Gaussian process (Snoek et al., 2012) to find the optimal hyperparameters such as the number of neurons, network depth or layers, initial learning rate etc. |