Counterexample-Guided Learning of Monotonic Neural Networks
Authors: Aishwarya Sivaraman, Golnoosh Farnadi, Todd Millstein, Guy Van den Broeck
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world datasets demonstrate that our approach achieves stateof-the-art results compared to existing monotonic learners, and can improve the model quality compared to those that were trained without taking monotonicity constraints into account. |
| Researcher Affiliation | Academia | Aishwarya Sivaraman University of California, Los Angeles dcssiva@cs.ucla.edu Golnoosh Farnadi Mila/Université de Montréal farnadig@mila.quebec Todd Millstein University of California, Los Angeles todd@cs.ucla.edu Guy Van den Broeck University of California, Los Angeles guyvdb@cs.ucla.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We have implemented these techniques in a tool called COMET.1 ... 1https://github.com/Aishwarya Sivaraman/COMET |
| Open Datasets | Yes | We use four datasets: Auto MPG and Boston Housing are regression datasets ... and are obtained from the UCI machine learning repository [6]; Heart Disease [19] and Adult [6] are classification datasets ... [6] Catherine L Blake and Christopher J Merz. Uci repository of machine learning databases, 1998, 1998. [19] John H. Gennari, Pat Langley, and Douglas H. Fisher. Models of incremental concept formation. Artif. Intell., 40(1-3):11 61, 1989. |
| Dataset Splits | No | We carry out our experiments on three random 80/20 splits and report average test results, except for the Adult dataset, for which we report on one random split. The paper specifies train/test splits but does not explicitly mention a validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | Experiments were implemented in Python using the Keras deep learning library [9], we use the ADAM optimizer [29] to perform stochastic optimization of the neural network models, and we use the Optimathsat [39] solver for counterexample generation. No version numbers are provided for Keras or Optimathsat. |
| Experiment Setup | Yes | For each dataset, we identify the best baseline architecture and parameters by conducting grid search and learn the best Re LU neural network (NNb). ... In this experiment we re-train NNb with counterexamples for 40 epochs, model selection is based on train quality... We tune Adam stepsize, learning rate, number of epochs, and batch size on all methods. |