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.
Fast Pareto Optimization for Subset Selection with Dynamic Cost Constraints
Authors: Chao Bian, Chao Qian, Frank Neumann, Yang Yu
IJCAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 17,753 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,155 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 | We prove that FPOMC can maintain the best known approximation guarantee efficiently.In this section, we prove the general approximation bound of FPOMC in Theorem 6, implying that FPOMC can achieve the best known polynomial-time approximation guarantee, i.e., (αf/2)(1 e αf ). |
| Researcher Affiliation | Academia | 1State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China 2Optimisation and Logistics, The University of Adelaide, Adelaide SA 5005, Australia |
| Pseudocode | Yes | Algorithm 1 FPOMC Algorithm, Algorithm 2 SELECT(P): Subroutine of FPOMC, Algorithm 3 LS(x): Subroutine of FPOMC |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the proposed methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments, therefore it does not refer to any train dataset or its accessibility. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, therefore it does not specify any validation dataset splits. |
| Hardware Specification | No | The paper does not conduct experiments and therefore does not specify any hardware used. |
| Software Dependencies | No | The paper does not conduct experiments and therefore does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include an experimental setup or details like hyperparameters. |