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..
Abduction-Based Explanations for Machine Learning Models
Authors: Alexey Ignatiev, Nina Narodytska, Joao Marques-Silva1511-1519
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results, obtained on well-known datasets, validate the scalability of the proposed approach as well as the quality of the computed solutions. |
| Researcher Affiliation | Collaboration | Alexey Ignatiev,1,3 Nina Narodytska,2 Joao Marques-Silva1 1Faculty of Science, University of Lisbon, Portugal 2VMware Research, CA, USA 3ISDCT SB RAS, Irkutsk, Russia EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Computing a subset-minimal explanation; Algorithm 2: Computing a smallest size explanation |
| Open Source Code | No | The paper mentions that its prototype implementation is "written in Python" and uses other open-source tools (e.g., Py SMT, RC2), but it does not provide an explicit statement or a link to its own source code for the methodology described in the paper. |
| Open Datasets | Yes | The benchmarks considered include the well-known text-based datasets from the UCI Machine Learning Repository1 and Penn Machine Learning Benchmarks2, as well as the widely used MNIST digits database3. ... 1https://archive.ics.uci.edu/ml/ 2https://github.com/EpistasisLab/penn-ml-benchmarks/ 3http://yann.lecun.com/exdb/mnist/ |
| Dataset Splits | No | The paper states that neural networks were trained on datasets and evaluated on samples, but it does not explicitly specify the training/validation/test split percentages, absolute sample counts for each split, or detailed splitting methodology needed for reproduction. |
| Hardware Specification | Yes | all benchmarks were ran on a Macbook Pro having an Intel Core i7 2.8GHz processor with 8GByte of memory on board. |
| Software Dependencies | Yes | CPLEX 12.8.0 (IBM ILOG 2018) is used as a MILP oracle accessed via its of๏ฌcial Python API. |
| Experiment Setup | Yes | Time limit was set to 1800 seconds while memory limit was set to 4GByte. ... Each neural network considered has one hidden layer with i {10, 15, 20} neurons. |