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.
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 | Input Tokens: 21,794 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,742 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 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. |