Summary of Risk-averse Learning with Delayed Feedback, by Siyi Wang et al.
Risk-averse learning with delayed feedback
by Siyi Wang, Zifan Wang, Karl Henrik Johansson, Sandra Hirche
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 Medium Difficulty summary: This paper explores risk-averse learning in real-world scenarios where decisions have delayed impacts. To accurately assess and manage risk, the authors propose two risk-averse learning algorithms that utilize Conditional Value at Risk (CVaR) as a risk measure, incorporating delayed feedback with unknown but bounded delays. The one-point and two-point zeroth-order optimization approaches are used to develop these algorithms. The regret achieved by each algorithm is analyzed in terms of cumulative delay and total samplings. Results show that the two-point risk-averse learning achieves a smaller regret bound than the one-point algorithm, while the one-point risk-averse learning attains sublinear regret under certain delay conditions. Numerical experiments on dynamic pricing problems demonstrate the effectiveness of these algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about making better decisions when we don’t see right away how they will turn out. The authors are trying to find a way to make sure that our decisions are good and that we’re not taking too much risk. They created two new ways to learn and make decisions, using something called Conditional Value at Risk (CVaR). These methods can handle delays in seeing the results of our decisions. The authors tested these methods on a problem about pricing things dynamically and found that they work well. |
Keywords
» Artificial intelligence » Optimization
