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..
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. |