Certified Monotonic Neural Networks
Authors: Xingchao Liu, Xing Han, Na Zhang, Qiang Liu
NeurIPS 2020 | Conference 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 xcliu@utexas.edu Xing Han Department of Electrical and Computer Engineering University of Texas at Austin Austin, TX 78712 aaronhan223@utexas.edu Na Zhang Tsinghua University zhangna@pbcsf.tsinghua.edu.cn Qiang Liu Department of Computer Science University of Texas at Austin Austin, TX 78712 lqiang@cs.utexas.edu |
| 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. |