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..
Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime
Authors: Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar
ICML 2023 | Venue PDF | 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. |