Data-Driven Conditional Robust Optimization
Authors: Abhilash Reddy Chenreddy, Nymisha Bandi, Erick Delage
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we use simulated and real world data to illustrate the implementation of our approach and compare it against two non-contextual robust optimization benchmark approaches to demonstrate the value of exploiting contextual information in robust optimization. In this section, we illustrate the coverage aspect of the IDCC approach using simulated data. We will further demonstrate the advantage of the CRO problem using a standard risk minimizing portfolio optimization problem. |
| Researcher Affiliation | Academia | Abhilash Chenreddy GERAD & Dept. of Decision Sciences HEC Montréal Montréal, Quebec, Canada abhilash.chenreddy@hec.ca Nymisha Bandi Mc Gill University Montréal, Quebec, Canada nymisha.bandi@mcgill.ca Erick Delage GERAD & Dept. of Decision Sciences HEC Montréal Montréal, Quebec, Canada erick.delage@hec.ca |
| Pseudocode | Yes | Algorithm 1 Integrated deep cluster-classify with deep K-means |
| Open Source Code | Yes | The code can be found on github3. Our code uses the Pytorch implementation from [Goerigk and Kurtz, 2020], which is available online4. 3https://anonymous.4open.science/r/Data-Driven-Conditional-Robust-Optimization-E160/ 4https://github.com/goerigk/RO-DNN |
| Open Datasets | Yes | Dataset Our experiments make use of historical data from the US stock market. We collect the adjusted daily closing prices for 70 stocks (as used in [Xu and Cohen, 2018]) coming from 8 different sectors from January 1, 2012, to December 31, 2020, using the Y!Finance s API. |
| Dataset Splits | Yes | Given the time series nature of the data, at a given instance, we use 3 years of data to train and the following year as validation to pick the hyperparameters of our model such as learning rate, weight decay, and the optimal number of clusters. We then retrain the model using the 4 years of data to build the final model. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | Yes | MOSEK Ap S. MOSEK Fusion API for C++ 9.3.20, 2022. URL https://docs.mosek.com/latest/cxxfusion/index.html. |
| Experiment Setup | Yes | We used learning rate = 0.01, αK = 0.5, αS = 0.5, β = 0.1 for all the experiments. |