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].
Bayesian Strategic Classification
Authors: Lee Cohen, Saeed Sharifi-Malvajerdi, Kevin Stangl, Ali Vakilian, Juba Ziani
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper does not include experiments. All results are carefully proven, and do not require experiments to establish correctness. |
| Researcher Affiliation | Academia | 1Stanford University. Email: EMAIL 2Toyota Technological Institute at Chicago (TTIC). Email: EMAIL, EMAIL, EMAIL 3Georgia Institute of Technology. Email: EMAIL |
| Pseudocode | Yes | Algorithm 1: Oracle(c, H) Algorithm 2: Best Response of Agents in the Linear Case Algorithm 3: The Learner s Optimization Problem: Discrete Uniform Prior |
| Open Source Code | No | The paper is theoretical and does not mention any open-source code release for the described methodology. |
| Open Datasets | No | The paper uses conceptual 'data distributions' (e.g., uniform distribution) for theoretical analysis, but does not refer to or provide access information for any publicly available or open dataset. |
| Dataset Splits | No | As a theoretical paper, it does not conduct experiments involving empirical data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments run on specific hardware. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or system-level training settings. |