Stateful Strategic Regression
Authors: Keegan Harris, Hoda Heidari, Steven Z. Wu
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Crucially, perhaps our most significant finding is that by considering the effects of multiple time-steps, the principal is significantly more effective at incentivizing the agent to accumulate effort in her desired direction (as demonstrated in Figure 1 for a stylized teacher-student example). |
| Researcher Affiliation | Academia | Keegan Harris School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 keeganh@cmu.edu Hoda Heidari School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 hheidari@cmu.edu Zhiwei Steven Wu School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 zstevenwu@cmu.edu |
| Pseudocode | Yes | Algorithm 1: Assessment Policy Recovery |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper discusses a 'classroom example' as a stylized case study but does not refer to the use of a public or open dataset with access information (e.g., links, DOIs, or specific citations). |
| Dataset Splits | No | The paper does not provide details on training, validation, or test dataset splits. The work is theoretical with a stylized example, not an empirical study requiring data partitioning. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU models, CPU types, or memory) used for running experiments or computations. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software libraries, tools, or programming languages used in the research. |
| Experiment Setup | No | The paper does not include specific details about an experimental setup, such as hyperparameter values, optimization settings, or other configuration information typical for empirical studies. |