Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Machine Learning for Integer Programming
Authors: Elias B. Khalil
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To illustrate the potential for ML in MIP, I have so far tackled branching variable selection, a crucial component of the search procedure, showing that ML approaches for variable selection can outperform traditional heuristics. ... Our approach, SB+ML, significantly outperforms competing methods, solving more instances than both PC and SB+PC (a hybrid of SB and PC), while also requiring around 36% and 16% fewer nodes on average, respectively. In terms of running time, our method incurs some additional overhead pernode due to feature computations, yet it can be 15 to 20% faster than the competitors overall on instances of medium and hard difficulties, thanks to large savings in the number of nodes processed. |
| Researcher Affiliation | Academia | Elias B. Khalil School of Computational Science & Engineering Georgia Institute of Technology EMAIL |
| Pseudocode | No | The paper describes the approaches but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper mentions collecting data for training ('the collected data being used for training') but does not provide concrete access information (e.g., link, citation to a specific dataset, or repository) for this data. It refers to a previous paper for experimental setup details. |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, or test splits. It states: 'We do not give details of the experimental setup here, and refer to our paper instead [Khalil et al., 2016].' |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory used for experiments. |
| Software Dependencies | No | The paper mentions commercial and non-commercial MIP solvers like IBM CPLEX and SCIP but does not specify their version numbers or any other software dependencies with version numbers used in their own experiments. |
| Experiment Setup | No | The paper explicitly states: 'We do not give details of the experimental setup here, and refer to our paper instead [Khalil et al., 2016].' Therefore, specific experimental setup details are not provided in this paper. |