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..
A Bandit Framework for Strategic Regression
Authors: Yang Liu, Yiling Chen
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | For linear regression and a certain family of non-linear regression problems, we show that SR-UCB enables an O p log T/T -Bayesian Nash Equilibrium (BNE) where each worker exerts a target effort level that the learner has chosen, with T being the number of data acquisition stages. |
| Researcher Affiliation | Academia | Yang Liu and Yiling Chen School of Engineering and Applied Science, Harvard University EMAIL |
| Pseudocode | Yes | Algorithm 1 SR-UCB: Worker index & selection |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes a theoretical framework and does not specify or provide access information for a publicly available dataset used for training. |
| Dataset Splits | No | The paper focuses on theoretical analysis and does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes a theoretical framework and does not detail an empirical experimental setup, including hyperparameters or system-level training settings. |