On Robustness to Adversarial Examples and Polynomial Optimization
Authors: Pranjal Awasthi, Abhratanu Dutta, Aravindan Vijayaraghavan
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate the effectiveness of these attacks on real data. |
| Researcher Affiliation | Academia | Pranjal Awasthi Department of Computer Science Rutgers University pranjal.awasthi@rutgers.edu Abhratanu Dutta Department of Computer Science Northwestern University abhratanudutta2020@u.northwestern.edu Aravindan Vijayaraghavan Department of Computer Science Northwestern University aravindv@northwestern.edu |
| Pseudocode | Yes | Figure 1: The SDP-based algorithm for the degree-2 optimization problem. ... Figure 2: Convex program to find a PTF sgn(g(x)) œ F with zero robust empirical error. ... Figure 3: The SDP-based algorithm for Problem (2). |
| Open Source Code | No | No statement about open-sourcing code or links to a repository are provided. The paper mentions future work related to making the analysis practical. |
| Open Datasets | Yes | We use the MNIST data set |
| Dataset Splits | No | The paper does not provide specific percentages or counts for training, validation, and test splits. It mentions dividing a 'test set' into PGDPass and PGDfail for their specific experiments, but not the overall dataset splits for reproduction. |
| Hardware Specification | No | The SDP has d + k + 1 vector variables, and takes about 200s per instance on a standard desktop.' The term 'standard desktop' is too vague and lacks specific hardware details. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, specific SDP solvers). |
| Experiment Setup | Yes | Our 2-layer neural network has d = 784 input units, k = 1024 hidden units and 10 output units. ... As in [24] we first choose = 0.3 ... We also run the PGD attack on the network with = 0.01. |