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..
Adversarially Robust Streaming Algorithms via Differential Privacy
Authors: Avinatan Hasidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main conceptual contribution is to show that the notion of differential privacy can be used as a tool in order to construct new adversarially robust streaming algorithms. This connection allows us to design new adversarially robust streaming algorithms that outperform the current state-of-the-art constructions for many interesting regimes of parameters. |
| Researcher Affiliation | Collaboration | Avinatan Hassidim Bar-Ilan University and Google. Haim Kaplan Tel Aviv University and Google. Yishay Mansour Google. Yossi Matias Google. Uri Stemmer Ben-Gurion University and Google. |
| Pseudocode | Yes | Algorithm 1 Robust Sketch (Page 6) |
| Open Source Code | No | The paper does not provide any information about open-source code for the described methodology or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets, thus no information on training datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental setups involving data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or system-level training settings. |