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..
(De-)Randomized Smoothing for Decision Stump Ensembles
Authors: Miklós Horváth, Mark Müller, Marc Fischer, Martin Vechev
NeurIPS 2022 | Venue PDF | 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 EMAIL, EMAIL |
| 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. |