Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
The Price of Differential Privacy under Continual Observation
Authors: Palak Jain, Sofya Raskhodnikova, Satchit Sivakumar, Adam Smith
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 38,205 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,459 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide the ο¬rst strong lower bounds on the error of continual release mechanisms. In particular, for two fundamental problems that are closely related to empirical risk minimization and widely studied and used in the standard (batch) model, we prove that the worst case error of every continual release algorithm is β¦(T 1/3) times larger than that of the best batch algorithm. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Boston University, Boston, Massachusetts, USA. |
| Pseudocode | Yes | Algorithm 1 Privacy game Ξ M,Adv for the continual release model with adaptively chosen inputs |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on proofs and algorithm design, not empirical evaluation on datasets. Therefore, it does not mention publicly available datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments with datasets, so it does not specify training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and does not involve experimental evaluation requiring specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments with specific software implementations or dependencies. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, thus no experimental setup details like hyperparameters or training settings are provided. |