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..
Robustness Verification of Tree-based Models
Authors: Hongge Chen, Huan Zhang, Si Si, Yang Li, Duane Boning, Cho-Jui Hsieh
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On RF/GBDT models trained on 10 datasets, our algorithm is hundreds of times faster than a previous approach that requires solving MILPs, and is able to give tight robustness verification bounds on large GBDTs with hundreds of deep trees. |
| Researcher Affiliation | Collaboration | Hongge Chen*,1 Huan Zhang*,2 Si Si3 Yang Li3 Duane Boning1 Cho-Jui Hsieh2,3 1Department of EECS, MIT 2Department of Computer Science, UCLA 3Google Research |
| Pseudocode | Yes | Algorithm 1: Enumerating all K-cliques on a K-partite graph with a known boxicity d; Algorithm 2: Multi-level verification framework |
| Open Source Code | Yes | Our code (XGBoost compatible) is available at https://github.com/chenhongge/treeVerification. |
| Open Datasets | Yes | We evaluate our proposed method for robustness verification of tree ensembles on two tasks: binary and multiclass classification on 9 public datasets including both small and large scale datasets... The datasets other than MNIST and Fashion MNIST are from LIBSVM [8]. |
| Dataset Splits | No | The paper does not explicitly state the use of a distinct validation dataset split with specific percentages or counts. It mentions training and testing. |
| Hardware Specification | Yes | We run our experiments on Intel Xeon Platinum 8160 CPUs. |
| Software Dependencies | No | The paper mentions using the 'XGBoost framework [12]' but does not provide a specific version number for it or any other software dependencies. |
| Experiment Setup | Yes | The number of trees in GBDTs and parameters used in training GBDTs for different datasets are shown in Table 3 in the appendix. |