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..
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. |