Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime
Authors: Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose new algorithms and lower bounds for the problems of differentially private online prediction from experts (DP-OPE) and differentially private online convex optimization (DP-OCO) in the realizable setting. |
| Researcher Affiliation | Collaboration | 1Apple 2Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel. |
| Pseudocode | Yes | Algorithm 1 Sparse-Vector for zero loss experts |
| Open Source Code | No | The paper does not provide any statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not describe experiments involving datasets for training or evaluation. Therefore, no access information for a public dataset is provided. |
| Dataset Splits | No | The paper is theoretical and does not discuss empirical experiments. Therefore, no information about training, validation, or test dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe the execution of experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe specific software implementations or list software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup or provide details such as hyperparameters or training configurations. |