Online Algorithms with Multiple Predictions
Authors: Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper studies online algorithms augmented with multiple machine-learned predictions. We give a generic algorithmic framework for online covering problems with multiple predictions that obtains an online solution that is competitive against the performance of the best solution obtained from the predictions. Our algorithm incorporates the use of predictions in the classic potential-based analysis of online algorithms. Our main technical result shows that the cost of the solution produced by the OCP algorithm is at most O(log k) times that of the DYNAMIC solution. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Duke University, Durham, NC, USA. 2Department of Computer Science and Engineering, IIT Delhi, New Delhi, India. |
| Pseudocode | Yes | Algorithm 1 Online Covering Algorithm; Algorithm 2 Algorithm for Online FACILITY LOCATION |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | This paper focuses on theoretical algorithm design and analysis. It does not utilize or refer to any publicly available datasets for training or evaluation. |
| Dataset Splits | No | This paper is theoretical and does not describe any experiments involving training, validation, or testing dataset splits. |
| Hardware Specification | No | This paper focuses on theoretical contributions and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper discusses theoretical algorithms and their analysis, but does not specify any software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and does not detail any experimental setup, hyperparameters, or system-level training settings. |