Summary of Adversarial Online Learning with Temporal Feedback Graphs, by Khashayar Gatmiry et al.
Adversarial Online Learning with Temporal Feedback Graphs
by Khashayar Gatmiry, Jon Schneider
First submitted to arxiv on: 30 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 A novel learning algorithm for prediction with expert advice is introduced in this paper, which allows the learner’s action at time t to depend only on losses from a specific subset of rounds. The structure of which rounds’ losses are visible is provided by a directed feedback graph known to the learner. A strategy for partitioning losses across sub-cliques of this graph is presented as the basis for the algorithm. A lower bound that is tight in many practical settings is also introduced, and it is conjectured to be within a constant factor of optimal. For transitive feedback graphs, the algorithm is efficiently implementable and achieves the optimal regret bound (up to a universal constant). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers came up with a new way for machines to learn from expert advice. They created an algorithm that lets the machine look at only some of the past mistakes (or “losses”) when making its next decision. The key is in understanding which losses are visible and how they’re connected. A clever strategy helps break down these connections into smaller groups, allowing the algorithm to work efficiently. This new approach has great potential for improving the performance of machines learning from expert advice. |
