Adapting to function difficulty and growth conditions in private optimization
Authors: Hilal Asi, Daniel Levy, John C. Duchi
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We develop algorithms for private stochastic convex optimization that adapt to the hardness of the specific function we wish to optimize. ... Our algorithms build upon the inverse sensitivity mechanism... We complement our algorithms with matching lower bounds... (Self-assessment: If you ran experiments... [N/A]) |
| Researcher Affiliation | Academia | Hilal Asi Daniel Levy John C. Duchi {asi,danilevy,jduchi}@stanford.edu |
| Pseudocode | Yes | Algorithm 1 Localization-based Algorithm and Algorithm 2 Epoch-based algorithms for κ-growth are present in the paper. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or links to a code repository. The self-assessment under 'If you are including theoretical results...' and 'If you ran experiments...' sections mark code availability as '[N/A]'. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using datasets. The self-assessment questions for 'If you ran experiments...' and 'If you are using existing assets...' are all marked as '[N/A]' regarding data. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments, thus it does not provide details on training, validation, or test dataset splits. The self-assessment questions for 'If you ran experiments...' are all marked as '[N/A]'. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments, therefore it does not specify any hardware used. The self-assessment question 'Did you include the total amount of compute and the type of resources used?' is marked '[N/A]'. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments, therefore it does not specify any software dependencies with version numbers. The self-assessment questions related to experiments are marked '[N/A]'. |
| Experiment Setup | No | The paper is theoretical and does not describe any experiments, therefore it does not provide specific experimental setup details such as hyperparameters or training configurations. The self-assessment questions related to experiments are marked '[N/A]'. |