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..
Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization
Authors: Bryan Wilder, Bistra Dilkina, Milind Tambe1658-1665
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results across a variety of domains show that decisionfocused learning often leads to improved optimization performance compared to traditional methods. We conduct experiments across a variety of domains in order to compare our decision-focused learning approach with traditional two stage methods. |
| Researcher Affiliation | Academia | Bryan Wilder, Bistra Dilkina, Milind Tambe Center for Artificial Intelligence in Society, University of Southern California EMAIL |
| Pseudocode | No | The paper describes the proposed methods and algorithms in narrative text but does not include any clearly labeled pseudocode blocks or algorithm figures. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing the source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Our experiments use the cora dataset (Sen et al. 2008). We consider a recommendation systems problem based on the Movielens dataset (Group Lens 2011). The ground truth matrices were generated using the Yahoo webscope (Yahoo 2007) dataset. |
| Dataset Splits | Yes | In each domain, we randomly divided the instances into 80% training and 20% test. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions software components like "Adam" for training and "metis" for partitioning, but it does not specify any version numbers for these or other software dependencies. |
| Experiment Setup | Yes | All networks used Re LU activations. All networks were trained using Adam with learning rate 10 3. We experimented with networks with 1 layer... and 2-layer networks, where the hidden layer (of size 200) gives additional expressive power. |