Fast Provably Robust Decision Trees and Boosting
Authors: Jun-Qi Guo, Ming-Zhuo Teng, Wei Gao, Zhi-Hua Zhou
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to support our approaches; in particular, our approaches are superior to those unprovably robust methods, and achieve better or comparable performance to those provably robust methods yet with the smallest running time. |
| Researcher Affiliation | Academia | 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China. |
| Pseudocode | Yes | Algorithm 1 Fast Provably Robust Decision Tree (FPRDT) |
| Open Source Code | No | The paper provides links to the code of *other* methods (e.g., RIGBT-h3, TREANT4, GROOT5, ROCT5, PRB tree6, RGBDT7), but does not explicitly state that the source code for the methodology described in *this* paper (FPRDT or PRAda Boost) is publicly available or provides a link to it. |
| Open Datasets | Yes | 1https://www.openml.org/ 2https://www.cs.toronto.edu/ kriz/cifar.html |
| Dataset Splits | Yes | The performances of the compared methods are evaluated by five trials of 5-fold cross validation, where test adversarial accuracies are obtained by averaging over these 25 runs, as summarized in Table 3. |
| Hardware Specification | Yes | experiments are performed with Python on nodes of a computational cluster with 20 CPUs (Intel Core i9-10900X 3.7GHz), running Ubuntu with 128GB main memory. |
| Software Dependencies | No | The paper mentions "Python" but does not specify a version number or list specific library names with their versions. |
| Experiment Setup | Yes | We take the maximum depth 4 for TREANT and ROCT due to high computational complexity, and do not restrict the depth for other methods. We set 10 as the minimum number of instances for a splitting leaf node, and each leaf node has at least 5 instances. |