Task-based End-to-end Model Learning in Stochastic Optimization
Authors: Priya Donti, Brandon Amos, J. Zico Kolter
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present three experimental evaluations of the proposed approach: a classical inventory stock problem, a real-world electrical grid scheduling task, and a real-world energy storage arbitrage task. We show that the proposed approach can outperform both traditional modeling and purely black-box policy optimization approaches in these applications. |
| Researcher Affiliation | Academia | Priya L. Donti Dept. of Computer Science Dept. of Engr. & Public Policy Carnegie Mellon University Pittsburgh, PA 15213 pdonti@cs.cmu.edu Brandon Amos Dept. of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 bamos@cs.cmu.edu J. Zico Kolter Dept. of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 zkolter@cs.cmu.edu |
| Pseudocode | Yes | Algorithm 1 Task Loss Optimization |
| Open Source Code | Yes | Source code for all experiments is available at https://github.com/locuslab/e2e-model-learning. |
| Open Datasets | No | The paper uses "real electrical grid data" but does not provide a specific link, DOI, or common name for public access to these datasets. |
| Dataset Splits | No | The paper specifies train and test data periods (e.g., "7 years of data to train the model, and 1.75 subsequent years for testing"), but does not explicitly mention or detail a separate validation split or set for hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.x, TensorFlow 2.x, PyTorch 1.x). |
| Experiment Setup | Yes | We employ a 2-hidden-layer neural network for this purpose, with an additional residual connection from the inputs to the outputs initialized to the linear regression solution. |