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..
Learning Global Transparent Models consistent with Local Contrastive Explanations
Authors: Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now empirically validate our method. We first describe the setup, followed by a discussion of the experimental results. |
| Researcher Affiliation | Industry | Tejaswini Pedapati IBM Research EMAIL Avinash Balakrishnan IBM Research EMAIL Karthikeyan Shanmugan IBM Research EMAIL Amit Dhurandhar IBM Research EMAIL |
| Pseudocode | Yes | Algorithm 1 Global Boolean Feature Learning (GBFL)., Algorithm 2 KDE based Grid Point Generation (GPG)., Algorithm 3 Model Generation using Local Explanations. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology, nor does it explicitly state that the code is available or will be released. |
| Open Datasets | Yes | We experimented on six publicly available datasets from Kaggle and UCI repository namely; Sky Survey, Credit Card, Magic, Diabetes, Waveform and WDBC. |
| Dataset Splits | Yes | Statistically significant results that measure performance based on paired t-test are reported, which are computed over 5 randomizations with 75/25% train/test split. 10-fold cross-validation was used to find all parameters including tree heights ( ≤ 5) for DT. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper mentions software components like 'Decision trees (DTs)' and 'CART algorithm' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Decision trees (DTs) with height 5 were the transparent learner based on the CART algorithm. ... 10-fold cross-validation was used to find all parameters including tree heights ( ≤ 5) for DT. |