Applied Online Algorithms with Heterogeneous Predictors
Authors: Jessica Maghakian, Russell Lee, Mohammad Hajiesmaili, Jian Li, Ramesh Sitaraman, Zhenhua Liu
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Theoretical results are supplemented by large-scale empirical evaluations with production data demonstrating the success of our methods on optimization problems occurring in large distributed computing systems. |
| Researcher Affiliation | Collaboration | 1Department of Applied Mathematics & Statistics, Stony Brook University, Stony Brook, NY, USA 2Manning College of Information and Computer Sciences, UMass Amherst, Amherst, MA, USA 3Department of Electrical and Computer Engineering, SUNY Binghamton, Binghamton, NY, USA 4Akamai Tech, Cambridge, MA, USA. |
| Pseudocode | Yes | Algorithm 1 par SBO, Algorithm 2 in DBO, Algorithm 3 Ro BO, Algorithm 4 Greedy Subroutine, Algorithm 5 DBO |
| Open Source Code | No | The paper provides a link to a dataset and related experiments, but not a general statement or link for the open-source code of the described methodologies (par SBO, in DBO, Ro BO). |
| Open Datasets | Yes | Dataset 2: The Impact of COVID-19 Lockdowns... We digitized publicly available records of public peering traffic at Milan s internet exchange to recover demand data... Dataset 2 and related experiments are available at https://github.com/jmaghakian/covid_exp |
| Dataset Splits | Yes | The data spans one month at five minute resolution and we use two weeks for training predictors and two weeks for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running experiments. |
| Software Dependencies | No | The paper mentions 'sktime' and 'Auto ARIMA forecaster' but does not provide specific version numbers for these software dependencies or other key libraries. |
| Experiment Setup | Yes | To generate the parameter predictions, we use linear regression with price setting, metro-area location and size as the dependent variables. For the input predictions, we predicted day-ahead windows using the Auto ARIMA forecaster available through sktime. The classifier was trained using the same features as the parameter predictions, with the best choice of DBO, par SBO or in DBO on historical data as labels for the training dataset. |