Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness
Authors: Zifeng Wang, Tong Jian, Aria Masoomi, Stratis Ioannidis, Jennifer Dy
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,209 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,297 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on multiple benchmark datasets and architectures demonstrate that incorporating an HSIC bottleneck regularizer attains competitive natural accuracy and improves adversarial robustness, both with and without adversarial examples during training. |
| Researcher Affiliation | Academia | Zifeng Wang Northeastern University EMAIL Tong Jian Northeastern University EMAIL Aria Masoomi Northeastern University EMAIL Stratis Ioannidis Northeastern University EMAIL Jennifer Dy Northeastern University EMAIL |
| Pseudocode | Yes | Algorithm 1: Robust Learning with HBa R Input: input sample tuples {(xi, yi)}n i=1, kernel function kx, ky, kz, a neural network hθ parameterized by θ, mini-batch size m, learning rate α. Output: parameter of classifier θ while θ has not converged do Sample a mini-batch of size m from input samples. Forward Propagation: calculate zi and hθ(x). Compute kernel matrices for X, Y and Zi using kx, ky, kz respectively inside mini-batch. Compute L(θ) via (6), where HSIC is evaluated empirically via (5). Backward Propagation: θ θ α L(θ). end |
| Open Source Code | Yes | Our code and adversarially robust models are publicly available.2 https://github.com/neu-spiral/HBa R |
| Open Datasets | Yes | We experiment with three standard datasets, MNIST [14], CIFAR-10 [12] and CIFAR-100 [12]. |
| Dataset Splits | No | No explicit mention of validation dataset splits (percentages or counts) or methodology to create them is found in the paper. |
| Hardware Specification | Yes | All experiments are carried out on a Tesla V100 GPU with 32 GB memory and 5120 cores. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., programming language, deep learning framework, or library versions) are mentioned in the paper. |
| Experiment Setup | Yes | Following prior literature, we set step size to 0.01 and radius r = 0.3 for MNIST, and step size as 2/255 and r = 8/255 for CIFAR-10 and CIFAR-100. ... We set λx as 1 and 0.006, λy as 50 and 0.05, for MNIST and CIFAR-10 respectively. |