Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights, and Algorithms
Authors: Chaosheng Dong, Bo Zeng
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results confirm the effectiveness of our model and the computational efficacy of algorithms. |
| Researcher Affiliation | Academia | 1Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, USA. |
| Pseudocode | Yes | Algorithm 1 Solving IMOP through clustering |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement, or mention of code in supplementary materials) for the source code of the described methodology. |
| Open Datasets | No | The paper mentions "Stock price data is scraped from S&P 500 Index. Quarterly portfolio data is scraped from the mutual fund VHCAX (Vanguard Capital Opportunity Fund Admiral Shares) and SWPPX (Schwab S&P 500 Stock Index) from March 2010 to December 2019." However, it does not provide concrete access information such as a specific link, DOI, repository name, or formal citation with authors/year for a publicly available dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, and test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions "Advanced optimization techniques, e.g., ADMM, are applied to enhance the efficiency of our algorithms" but does not provide specific software dependencies (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper states "Details of the experiments and the associated techniques can be seen in the supplementary material" but does not provide specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) within the main text. |