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..
On Robustness to Adversarial Examples and Polynomial Optimization
Authors: Pranjal Awasthi, Abhratanu Dutta, Aravindan Vijayaraghavan
NeurIPS 2019 | Venue PDF | 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 EMAIL Abhratanu Dutta Department of Computer Science Northwestern University EMAIL Aravindan Vijayaraghavan Department of Computer Science Northwestern University EMAIL |
| 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. |