Robustness Verification of Tree-based Models
Authors: Hongge Chen, Huan Zhang, Si Si, Yang Li, Duane Boning, Cho-Jui Hsieh
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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. |