Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization
Authors: Bryan Wilder, Bistra Dilkina, Milind Tambe1658-1665
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | 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 {bwilder, dilkina, tambe}@usc.edu |
| 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. |