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 [1].
Strategic Littlestone Dimension: Improved Bounds on Online Strategic Classification
Authors: Saba Ahmadi, Kunhe Yang, Hanrui Zhang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper does not include experiments. |
| Researcher Affiliation | Academia | Toyota Technological Institute at Chicago, EMAIL University of California, Berkeley, EMAIL Chinese University of Hong Kong, EMAIL |
| Pseudocode | Yes | Algorithm 1: The Strategic Standard Optimal Algorithm (SSOA) |
| Open Source Code | No | This paper does not include experiments requiring code. |
| Open Datasets | No | This paper does not include experiments, and therefore no specific dataset information or access is provided. |
| Dataset Splits | No | This paper does not include experiments, and therefore no specific dataset split information for validation is provided. |
| Hardware Specification | No | This paper does not include experiments, and therefore no specific hardware details are provided. |
| Software Dependencies | No | This paper does not include experiments, and therefore no specific software dependencies with version numbers are listed. |
| Experiment Setup | No | This paper does not include experiments, and therefore no specific experimental setup details or hyperparameters are provided. |