On Solution Functions of Optimization: Universal Approximation and Covering Number Bounds
Authors: Ming Jin, Vanshaj Khattar, Harshal Kaushik, Bilgehan Sel, Ruoxi Jia
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our results provide the first rigorous analysis of the approximation and learningtheoretic properties of solution functions with implications for algorithmic design and performance guarantees. |
| Researcher Affiliation | Academia | Ming Jin, Vanshaj Khattar, Harshal Kaushik, Bilgehan Sel, and Ruoxi Jia Electrical and Computer Engineering, Virginia Tech jinming@vt.edu, vanshajk@vt.edu, harshaldkaushik@vt.edu, bsel@vt.edu, ruoxijia@vt.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using a specific dataset or provide access information for any dataset. |
| Dataset Splits | No | The paper is theoretical and does not provide specific dataset split information needed to reproduce experiments. |
| Hardware Specification | No | The paper is theoretical and does not describe the hardware used to run experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not contain specific experimental setup details such as hyperparameter values or training configurations. |