Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Explanations for Monotonic Classifiers.
Authors: Joao Marques-Silva, Thomas Gerspacher, Martin C Cooper, Alexey Ignatiev, Nina Narodytska
ICML 2021 | Venue PDF | 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. |