Omnipredictors for Constrained Optimization
Authors: Lunjia Hu, Inbal Rachel Livni Navon, Omer Reingold, Chutong Yang
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | While our work is theoretical, we view it as giving a foundation and proof-of-concept for potential omnipredictors to be deployed in the real world with fairness considerations. |
| Researcher Affiliation | Academia | *Equal contribution 1Computer Science Department, Stanford University, Stanford, USA. Correspondence to: Lunjia Hu <lunjia@stanford.edu>. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode blocks or algorithm sections. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments involving specific public datasets or provide access information for any dataset used for training, validation, or testing. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental dataset splits. Therefore, it does not specify training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not involve experimental setups, hyperparameters, or training configurations. Therefore, no such details are provided. |