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..
Tight Bounds for Answering Adaptively Chosen Concentrated Queries
Authors: Emma Rapoport, Edith Cohen, Uri Stemmer
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper includes theorems, proofs, and theoretical analyses. For example, Section 1.1 Our results states: "We establish a new negative result providing strong evidence that the linear barrier discussed above is inherent." and "We present a significantly simpler analysis for their algorithm that does not use typical stability at all. Instead, it relies on techniques from differential privacy...". Furthermore, the NeurIPS Paper Checklist explicitly states for questions 4, 5, 6, 7, and 8: "The paper does not include experiments." and the answer is "[NA]" for these questions, indicating a purely theoretical work. |
| Researcher Affiliation | Collaboration | Emma Rapoport is affiliated with "Tel Aviv University" (Academic). Edith Cohen is affiliated with "Google Research and Tel Aviv University", with the first listed being "Google Research" (Industry). Uri Stemmer is affiliated with "Tel Aviv University and Google Research", with the first listed being "Tel Aviv University" (Academic). The presence of both academic (Tel Aviv University) and industry (Google Research) affiliations indicates a collaborative effort. |
| Pseudocode | Yes | Algorithm 1 Attack ProcedureInitialization: Let M be an NS mechanism initialized with a sample S D.For each element xj1 X1, initialize an accumulated score Zj = 0.Information gathering rounds: For each round t [k]:1. Sample pt Unif[0, 1].2. Define the query qt : X {0, 1} as:/ Bernoulli(pt) if x X1, 0 otherwise.3. Submit qt to the mechanism and receive the response at.4. For each xj1 X1, define zjt = (at pt/r) (qt(xj1) pt), and update Zj Zj + zjt .Final query: After k rounds, compute j = arg maxj Zj. Submit a final query q : X {0, 1} by setting1 if x = xji for i [r], 0 otherwise. |
| Open Source Code | No | NeurIPS Paper Checklist, Question 5: "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: "[NA]" Justification: "The paper does not include experiments." |
| Open Datasets | No | NeurIPS Paper Checklist, Question 5: "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: "[NA]" Justification: "The paper does not include experiments." |
| Dataset Splits | No | NeurIPS Paper Checklist, Question 6: "Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?" Answer: "[NA]" Justification: "The paper does not include experiments." |
| Hardware Specification | No | NeurIPS Paper Checklist, Question 8: "For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?" Answer: "[NA]" Justification: "The paper does not include experiments." |
| Software Dependencies | No | NeurIPS Paper Checklist, Question 6: "Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?" Answer: "[NA]" Justification: "The paper does not include experiments." |
| Experiment Setup | No | NeurIPS Paper Checklist, Question 6: "Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?" Answer: "[NA]" Justification: "The paper does not include experiments." |