(De-)Randomized Smoothing for Decision Stump Ensembles
Authors: Miklós Horváth, Mark Müller, Marc Fischer, Martin Vechev
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An extensive experimental evaluation on computer vision and tabular data tasks shows that our approach yields significantly higher certified accuracies than the state-of-the-art for tree-based models.An extensive empirical evaluation, demonstrating the effectiveness of our approach and establishing a new state-of-the-art in a wide range of settings (Section 5). |
| Researcher Affiliation | Academia | Miklós Z. Horváth , Mark Niklas Müller , Marc Fischer, Martin Vechev Department of Computer Science ETH Zurich Switzerland mihorvat@ethz.ch, {mark.mueller,marc.fischer,martin.vechev}@inf.ethz.ch |
| Pseudocode | Yes | Algorithm 1 Stump Ensemble PDF computation via Dynamic Programming function COMPUTEPDF({(Γ, v)i}d i=1, x, ϕ) |
| Open Source Code | Yes | An extensive experimental evaluation on computer vision and tabular data tasks shows that our approach yields significantly higher certified accuracies than the state-of-the-art for tree-based models. We release all code and trained models at https://github.com/eth-sri/drs. |
| Open Datasets | Yes | We compare to prior work on the DIABETES [36], BREASTCANCER [37], FMNIST-SHOES [38], MNIST 1 VS. 5 [39], and MNIST 2 VS. 6 [39] datasets and are the first to provide joint certificates on a set of new benchmarks (Section 5.2). Finally, we perform an ablation study, investigating the effect of DRS s key components (Section 5.3).All datasets we use are publicly available. |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] We provide full training details in App. B.However, in App. C we report numerous error bars with respect to the data split via 5-fold cross-validation. |
| Hardware Specification | Yes | We implement our approach in Py Torch [35] and evaluate it on Intel Xeon Gold 6242 CPUs and an NVIDIA RTX 2080Ti. |
| Software Dependencies | No | We implement our approach in Py Torch [35]. It mentions PyTorch, but does not provide a specific version number for PyTorch or any other software dependency. |
| Experiment Setup | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] We provide full training details in App. B. |