Summary of Online Algorithms with Limited Data Retention, by Nicole Immorlica et al.
Online Algorithms with Limited Data Retention
by Nicole Immorlica, Brendan Lucier, Markus Mobius, James Siderius
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper introduces an online learning algorithm that operates under strict data retention constraints. The algorithm encounters a stream of data points, each with the ability to request removal from memory after a certain number of rounds. To model this removal process, the algorithm can only store a subset of data points and calculations between rounds. At the end of the stream, the algorithm must answer a statistical query about the full dataset. The paper explores the performance guarantees that can be achieved as a function of the retention constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed an online learning algorithm that can process data streams while following strict data retention rules. This means that each new piece of data can ask to be removed from memory after a certain number of rounds. To make this work, the algorithm only keeps track of some data points and calculations between rounds. When all the data is processed, the algorithm must answer a question about the entire dataset. The researchers are trying to figure out what level of performance they can expect based on how many data points they’re allowed to keep. |
Keywords
» Artificial intelligence » Online learning
