Adversarially Robust Dense-Sparse Tradeoffs via Heavy-Hitters
Authors: David Woodruff, Samson Zhou
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we describe our empirical evaluations for comparing the flip number of the entire vector and the flip number of the residual vector on real-world datasets. |
| Researcher Affiliation | Academia | David P. Woodruff Department of Computer Science Carnegie Mellon University dwoodruf@andrew.cmu.edu Samson Zhou Department of Computer Science Texas A&M University samsonzhou@gmail.com |
| Pseudocode | Yes | Algorithm 1 ROBUSTHH: Adversarially robust Lp-heavy hitters |
| Open Source Code | Yes | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [Yes] Justification: Yes, open access to the data and code are provided in the full version of the paper. |
| Open Datasets | Yes | CAIDA traffic monitoring dataset. We used the CAIDA dataset [CAI16] of anonymized passive traffic traces from the equinix-nyc data center s high-speed monitor. |
| Dataset Splits | No | The paper describes an empirical evaluation comparing flip numbers but does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | Yes | Our empirical evaluations were performed Python 3.10 on a 64-bit operating system on an AMD Ryzen 7 5700U CPU, with 8GB RAM and 8 cores with base clock 1.80 GHz. |
| Software Dependencies | No | The paper mentions 'Python 3.10' but does not list multiple key software components with their versions or a self-contained solver/package with a specific version number as required for a 'Yes' answer. |
| Experiment Setup | Yes | We compare the flip number of the entire data stream versus the flip number of the residual vector across various values of the algorithm error ε {10 1, 10 2, . . . , 10 5}, values of the heavy-hitter threshold α {4 1, 4 2, . . . , 4 10}, and the frequency moment parameter p {1.1, 1.2, . . . , 1.9}. |