Strategic Littlestone Dimension: Improved Bounds on Online Strategic Classification
Authors: Saba Ahmadi, Kunhe Yang, Hanrui Zhang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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, saba@ttic.edu University of California, Berkeley, kunheyang@berkeley.edu Chinese University of Hong Kong, hanrui@cse.cuhk.edu.hk |
| 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. |