Cardinality-Minimal Explanations for Monotonic Neural Networks
Authors: Ouns El Harzli, Bernardo Cuenca Grau, Ian Horrocks
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments suggest favourable performance of our algorithms.We conducted experiments on two partially monotonic datasets commonly used as benchmarks for designing monotonic and partially-monotonic models [Liu et al., 2020]: Blog Feedback Regression [Buza, 2014], a regression dataset with 276 features and Loan Defaulter1, a classification dataset with 28 features. |
| Researcher Affiliation | Academia | Ouns El Harzli , Bernardo Cuenca Grau , Ian Horrocks Department of Computer Science, University of Oxford {ouns.elharzli, bernardo.cuenca.grau, ian.horrocks}@cs.ox.ac.uk |
| Pseudocode | Yes | Algorithm 1 Computing contrastive explanations. |
| Open Source Code | No | To the best of our knowledge, our implementation is the only one available for computing cardinality-minimal explanations and hence we could not find a suitable benchmark for comparison. |
| Open Datasets | Yes | We conducted experiments on two partially monotonic datasets commonly used as benchmarks for designing monotonic and partially-monotonic models [Liu et al., 2020]: Blog Feedback Regression [Buza, 2014], a regression dataset with 276 features and Loan Defaulter1, a classification dataset with 28 features.1https://www.kaggle.com/datasets/wordsforthewise/lending- |
| Dataset Splits | No | We trained monotonic FCN models... We were able to reach a root mean-squared error (RMSE) of 0.175 on the test set for the Blog Feedback regression... and reached an accuracy of 60% on Loan Defaulter... (No mention of validation split or specific train/test percentages/counts.) |
| Hardware Specification | No | All experiments were conducted using Google Colab with GPU. (This does not specify the model of the GPU or any CPU details.) |
| Software Dependencies | No | We trained monotonic FCN models on both datasets with Py Torch [Paszke et al., 2019] using the mean-squared error loss for the Blog Feedback dataset and the binary cross entropy loss for the Loan Defaulter dataset. We trained the models with Adam [Kingma and Ba, 2014] for 10 epochs, setting all negative weights to 0 after each iteration of Adam to ensure monotonicity. (No specific version numbers for PyTorch, Adam, or any other software dependencies are provided.) |
| Experiment Setup | Yes | We trained monotonic FCN models on both datasets with Py Torch [Paszke et al., 2019] using the mean-squared error loss for the Blog Feedback dataset and the binary cross entropy loss for the Loan Defaulter dataset. We trained the models with Adam [Kingma and Ba, 2014] for 10 epochs, setting all negative weights to 0 after each iteration of Adam to ensure monotonicity. |