Adversarial Robustness of Streaming Algorithms through Importance Sampling
Authors: Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically confirm the robustness of our algorithms on various adversarial attacks and demonstrate that by contrast, some common existing algorithms are not robust. |
| Researcher Affiliation | Collaboration | Vladimir Braverman Google vbraverman@google.com Avinatan Hassidim Google avinatan@google.com Yossi Matias Google yossi@google.com Mariano Schain Google marianos@google.com Sandeep Silwal MIT silwal@mit.edu Samson Zhou Carnegie Mellon University samsonzhou@gmail.com |
| Pseudocode | Yes | Algorithm 1 Row sampling based algorithms, e.g., [CMP16, BDM+20] |
| Open Source Code | No | The paper mentions using and comparing against existing implementations and their 'own row sampling based implementation' but does not provide any link or explicit statement about making their source code publicly available. |
| Open Datasets | No | The paper describes generating its own data for adversarial settings (e.g., 'randomly sampled from a two dimensional standard normal distribution'), but it does not provide access information (link, DOI, citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes the data generation for its adversarial experiments but does not specify training, validation, or test split percentages, sample counts, or reference predefined splits with citations for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using specific software like 'Spark' [ZXW+16b], 'Stream KM++' [AMR+12], and 'River' [MHM+20] but does not provide specific version numbers for these or any other software dependencies, which are necessary for reproducibility. |
| Experiment Setup | No | The paper describes the adversarial data generation process for its experiments but does not provide specific hyperparameters (e.g., learning rate, batch size, epochs) or detailed training configurations for the algorithms used or compared. |