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 the Robustness of CountSketch to Adaptive Inputs
Authors: Edith Cohen, Xin Lyu, Jelani Nelson, Tamas Sarlos, Moshe Shechner, Uri Stemmer
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our analysis with empirical evaluation, showing that our attack is practical. ... Code for all the experiments is available at https: //github.com/google-research/google-research/ tree/master/robust_count_sketch. |
| Researcher Affiliation | Collaboration | 1Google Research 2Tel Aviv University 3UC Berkeley. |
| Pseudocode | Yes | Algorithm 1: Threshold Monitor (34) ... Algorithm 2: Robust Threshold BCount Sketch Estimator ... Algorithm 3: Weight Estimator ... Algorithm 4: Robust BCount Sketch: Streaming |
| Open Source Code | Yes | Code for all the experiments is available at https: //github.com/google-research/google-research/ tree/master/robust_count_sketch. |
| Open Datasets | No | The paper describes generating input vectors for simulation ('input vectors with one 'heavy' entry and n i.i.d. entries N(0, 1)') but does not explicitly state the use of a publicly available or open dataset, nor does it provide a link or citation for one. |
| Dataset Splits | No | The paper does not provide explicit training/test/validation dataset splits for its own experiments. It only mentions 'simulation results' and describes how input vectors were generated. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for its dependencies. |
| Experiment Setup | Yes | We performed additional simulations (not shown) where we swept b from 30 to 300, keeping d/b = 100 and keeping k /b = 3. ... The evaluation used input vectors with one 'heavy' entry and n i.i.d. entries N(0, 1). ... Sketch parameters b = 7, d/b = 101, left: n = 5 104, right: n = 1 105. (Figure 2 caption) ... We use b = 7. Left: v = 20, n = 5 103 middle: v = 25, n = 1 104 right: v = 70, n = 5 104. (Figure 3 caption) |