Explanations for Monotonic Classifiers.
Authors: Joao Marques-Silva, Thomas Gerspacher, Martin C Cooper, Alexey Ignatiev, Nina Narodytska
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 4 summarizes initial experiments, which confirm the scalability of the proposed algorithms. |
| Researcher Affiliation | Collaboration | 1IRIT, CNRS, Universit e Paul Sabatier, Toulouse, France 2Monash University, Melbourne, Australia 3VMware Research, CA, USA. |
| Pseudocode | Yes | Algorithm 1 Finding one AXp find AXp(F, S, v); Algorithm 2 Finding one CXp find CXp(F, S, v); Algorithm 3 Enumeration of AXp s and CXp s |
| Open Source Code | Yes | XMono is available from https://git.io/JZZBX. |
| Open Datasets | Yes | COMET is run on the Auto-MPG11 dataset studied in earlier work (Sivaraman et al., 2020)... We use a monotonic subset (Pima Mono) of the Pima dataset12... The first dataset is Bankruptcy Risk (Greco et al., 1998)... 11http://tiny.cc/k3qytz. 12http://tiny.cc/l3qytz. |
| Dataset Splits | No | For each dataset, we either pick 100 instances, randomly selected, or the total number of instances in the dataset, in case this number does not exceed 100. |
| Hardware Specification | Yes | All experiments were run on a Mac Book Pro, with a 2.4GHz quad-core i5 processor, and 16 GByte of RAM, running Mac OS Big Sur. |
| Software Dependencies | No | All experiments were run on a Mac Book Pro, with a 2.4GHz quad-core i5 processor, and 16 GByte of RAM, running Mac OS Big Sur. |
| Experiment Setup | No | For each dataset, we either pick 100 instances, randomly selected, or the total number of instances in the dataset, in case this number does not exceed 100. |