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..
Robust Decision Trees Against Adversarial Examples
Authors: Hongge Chen, Huan Zhang, Duane Boning, Cho-Jui Hsieh
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on real world datasets demonstrate that the proposed algorithms can substantially improve the robustness of tree-based models against adversarial examples. |
| Researcher Affiliation | Academia | 1MIT, Cambridge, MA 02139, USA 2UCLA, Los Angeles, CA 90095, USA. |
| Pseudocode | Yes | Algorithm 1 Robust Split with Information Gain; Algorithm 2 Robust Split for Boosted Tree |
| Open Source Code | Yes | Our code is at https://github.com/chenhongge/RobustTrees. |
| Open Datasets | Yes | We present results on three small datasets... We consider nine real world large or medium sized datasets and two small datasets (Chang & Lin, 2011), spanning a variety of data types (including both tabular and image data). [...] Figure 1. MNIST and Fashion-MNIST examples... [...] Table 2. Test accuracy and robustness of information gain based single decision tree model. [...] Table 3. The test accuracy and robustness of GBDT models. |
| Dataset Splits | Yes | In Table 2, we present the average ββ distortion of the adversarial examples of both classical natural decision trees and our robust decision trees trained on different datasets. [...] In Table 3, we present the average ββ distortion of adversarial examples found by Chengβs ββ attack for both natural GBDT and robust GBDT models trained on those datasets. |
| Hardware Specification | No | The paper does not explicitly mention any specific hardware (e.g., CPU, GPU models, or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like XGBoost, Light GBM, and Cat Boost, but does not provide specific version numbers for any of these dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | We use the same number of trees, depth and step size shrinkage as in Kantchelian et al. (2016) to train our robust and natural models. [...] Table 2 and 3 list 'depth' for the models, and Table 3 lists 'Ο΅' as a robust training hyper-parameter. |