Online Algorithms with Uncertainty-Quantified Predictions
Authors: Bo Sun, Jerry Huang, Nicolas Christianson, Mohammad Hajiesmaili, Adam Wierman, Raouf Boutaba
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 1 compares the empirical competitive ratios (CRs) of our proposed online algorithms in the setting of a multiple-instance ski rental problem. The setup details can be found in Appendix D.6. |
| Researcher Affiliation | Academia | 1David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada. 2Computing + Mathematical Sciences Department, California Institute of Technology, Pasadena, USA. 3Manning College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, USA. |
| Pseudocode | Yes | Algorithm 1 DSR: Deterministic algorithm for ski rental; Algorithm 2 RSR(y): Randomized algorithm for ski rental; Algorithm 3 PFA(G): Protection-function-based algorithm; Algorithm 4 Online deterministic algorithm with PIP for ski rental; Algorithm 5 Online learning algorithm with uncertainty-quantified predictions; Algorithm 6 Online learning algorithm for multiple-instance ski rental |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes generating synthetic data for its experiments: "We generate T = 3000 instances, each with true skiing days nt sampled uniformly at random from {1, . . . , 8}." It does not refer to a publicly available or open dataset with access information. |
| Dataset Splits | No | The paper describes the generation of instances for its online algorithm evaluations but does not specify traditional training, validation, or test splits for a machine learning model. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not list any specific software components with version numbers. |
| Experiment Setup | Yes | Setup. We set buying cost to B = 2. We generate T = 3000 instances, each with true skiing days nt sampled uniformly at random from {1, . . . , 8}. Synthetic PIP predictions are generated by sampling a point pt from a normal distribution N(nt, σ2 t ) and then taking the 90% confidence interval (ℓt, ut) = (pt z0.95σt, pt + z0.95σt). Here, σt is set to simulate oscillating good and bad predictors: the first 10 instances have σt = 0, followed by the next 10 with σt = 6, and repeating. |