Certified Monotonic Neural Networks
Authors: Xingchao Liu, Xing Han, Na Zhang, Qiang Liu
NeurIPS 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical studies on various datasets demonstrate the efficiency of our approach over the state-of-the-art methods, such as Deep Lattice Networks [34]. |
| Researcher Affiliation | Academia | Xingchao Liu Department of Computer Science University of Texas at Austin Austin, TX 78712 EMAIL Xing Han Department of Electrical and Computer Engineering University of Texas at Austin Austin, TX 78712 EMAIL Na Zhang Tsinghua University EMAIL Qiang Liu Department of Computer Science University of Texas at Austin Austin, TX 78712 EMAIL |
| Pseudocode | Yes | See in Algorithm 1 in Appendix for the detailed procedure. |
| Open Source Code | Yes | The code is publicly available3. https://github.com/gnobitab/Certified Monotonic Network |
| Open Datasets | Yes | Experiments are performed on 4 datasets: COMPAS [16], Blog Feedback Regression [4], Loan Defaulter1, Chest X-ray2. 1https://www.kaggle.com/wendykan/lending-club-loan-data 2https://www.kaggle.com/nih-chest-xrays/sample |
| Dataset Splits | Yes | For each dataset, we pick 20% of the training data as the validation set. 20% of the training data is used as the validation set. |
| Hardware Specification | Yes | Our computer has 48 cores and 192GB memory. |
| Software Dependencies | Yes | For solving the MILP problems, we adopt Gurobi v9.0.1 [14], which is an efficient commercial solver. Our method is implemented with Py Torch [24]. |
| Experiment Setup | Yes | We use crossentropy loss for classification problems, and mean-squareerror for regression problems. Adam [18] optimizer is used for optimization. We initialize the coefficient of monotonicity regularization λ = 1, and multiply λ by 10 every time λ needs amplification. The default learning rate is 5e 3. |