Summary of An Axiomatic Approach to Loss Aggregation and An Adapted Aggregating Algorithm, by Armando J. Cabrera Pacheco and Rabanus Derr and Robert C. Williamson
An Axiomatic Approach to Loss Aggregation and an Adapted Aggregating Algorithm
by Armando J. Cabrera Pacheco, Rabanus Derr, Robert C. Williamson
First submitted to arxiv on: 4 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 This paper explores online learning under expert advice, departing from traditional risk minimization frameworks. It introduces more general aggregation functions for losses incurred by learners, which are characterized as quasi-sums through easily justified assumptions. The authors propose a variant of the Aggregating Algorithm adapted to these novel aggregation functions, inheriting desirable theoretical properties such as Bayes’ updating and time-independent regret bounds. This work highlights the learner’s attitude towards losses, emphasizing the significance of generalized aggregations in understanding online learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about how we can learn from expert advice while making decisions online. It looks at how we combine different types of mistakes (losses) to make better choices over time. The authors develop a new algorithm that works well with these combined loss functions, which helps us understand how our attitude towards losses affects our learning process. |
Keywords
» Artificial intelligence » Online learning
