Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Omnipredictors for Constrained Optimization
Authors: Lunjia Hu, Inbal Rachel Livni Navon, Omer Reingold, Chutong Yang
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 41,317 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,829 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 <EMAIL>. |
| 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. |