Summary of Distributed No-regret Learning For Multi-stage Systems with End-to-end Bandit Feedback, by I-hong Hou
Distributed No-Regret Learning for Multi-Stage Systems with End-to-End Bandit Feedback
by I-Hong Hou
First submitted to arxiv on: 6 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Networking and Internet Architecture (cs.NI)
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| 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 proposes novel distributed online learning algorithms for multi-stage systems with end-to-end bandit feedback, which enables multiple agents to learn from outcomes while lacking knowledge or control over actions taken by subsequent agents. The proposed algorithms aim to achieve sublinear regret in adversarial environments, demonstrating improved performance and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The goal of this paper is to develop distributed online learning algorithms that can efficiently learn from end-to-end bandit feedback in multi-stage systems. This approach enables multiple agents to make decisions without knowing the outcome or having control over actions taken by subsequent agents. |
Keywords
* Artificial intelligence * Online learning
