A Bandit Framework for Strategic Regression
Authors: Yang Liu, Yiling Chen
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | 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 {yangl,yiling}@seas.harvard.edu |
| 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. |