Summary of Randomized Confidence Bounds For Stochastic Partial Monitoring, by Maxime Heuillet et al.
Randomized Confidence Bounds for Stochastic Partial Monitoring
by Maxime Heuillet, Ola Ahmad, Audrey Durand
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 explores the partial monitoring (PM) framework, which models sequential learning problems with incomplete feedback. The PM setting involves an agent playing actions while observing outcomes and receiving partial feedback signals. The goal is to minimize the cumulative loss by leveraging the received feedback. The paper considers both contextual and non-contextual PM settings with stochastic outcomes. It introduces new strategies based on randomizing deterministic confidence bounds, extending regret guarantees to scenarios where existing methods are not applicable. Experimental results demonstrate favorable performance of proposed RandCBP and RandCBPsidestar strategies against state-of-the-art baselines in multiple PM games. The paper also advocates for the adoption of the PM framework by designing a real-world use case on monitoring error rates in deployed classification systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how machines can learn from incomplete information. Imagine you’re trying to make predictions about what will happen next based on some clues, but those clues aren’t always accurate. The partial monitoring (PM) framework helps solve this problem by developing strategies for making good decisions when feedback is limited. The researchers propose new ways of combining old ideas to improve performance in different situations. They test their ideas and show that they work well compared to existing methods. This could have important applications, such as helping machines monitor the accuracy of predictions made by other systems. |
Keywords
* Artificial intelligence * Classification
