Hierarchical Randomized Smoothing
Authors: Yan Scholten, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally demonstrate the importance of hierarchical smoothing in image and node classification, where it yields superior robustness-accuracy trade-offs. |
| Researcher Affiliation | Academia | 1Dept. of Computer Science & Munich Data Science Institute, Technical University of Munich 2University of Cologne, Germany |
| Pseudocode | Yes | Algorithm 1: Binary-class Robustness Certificates for Hierarchical Randomized Smoothing Algorithm 2: Multi-class Robustness Certificates for Hierarchical Randomized Smoothing |
| Open Source Code | Yes | Project page: https://www.cs.cit.tum.de/daml/hierarchical-smoothing To ensure reproducibility we further use random seeds for all random processes including for model training, for drawing smoothing parameters and for drawing Monte-Carlo samples. We publish the source code including reproducibility instructions and all random seeds. |
| Open Datasets | Yes | For image classification we train Res Net50 (He et al., 2016) on CIFAR10 (Krizhevsky et al., 2009)... For node classification we train graph attention networks (GATs) (Velickovic et al., 2018) with two layers on Cora-ML (Mc Callum et al., 2000; Bojchevski and Günnemann, 2018)... The graph datasets are publicly available and can be downloaded also e.g. using Py Torch Geometric. The CIFAR10 dataset is also publicly available, for example via the torchvision library. |
| Dataset Splits | Yes | We label 20 randomly drawn nodes per class for training and validation, and 10% of the nodes for testing. We train our models on training nodes, tune them on validation nodes and compute certificates for all test nodes. |
| Hardware Specification | Yes | All experiments were performed on a Xeon E5-2630 v4 CPU with a NVIDIA GTX 1080TI GPU. |
| Software Dependencies | No | The paper mentions specific libraries like "Py Torch Geometric" and "torchvision library" and algorithms like "Adam", but does not provide specific version numbers for all key software components (e.g., PyTorch, Python versions). |
| Experiment Setup | Yes | At test time, we use significance level α = 0.01, n0 = 1,000 samples for estimating the majority class and n1 = 10,000 samples for certification. ... We train models full-batch using Adam (learning rate = 0.001, β1 =0.9, β2 =0.999, ϵ = e 08, weight decay = 5e 04) for a maximum of 1,000 epochs with cross-entropy loss. ... We train the Res Net50 model with stochastic gradient descent (learning rate 0.01, momentum 0.9, weight decay 5e-4) for 400 epochs using a batch-size of 128. |