Summary of Concentration Bounds For Optimized Certainty Equivalent Risk Estimation, by Ayon Ghosh et al.
Concentration Bounds for Optimized Certainty Equivalent Risk Estimation
by Ayon Ghosh, L.A. Prashanth, Krishna Jagannathan
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 A novel approach is proposed for estimating Optimized Certainty Equivalent (OCE) risk from independent and identically distributed (i.i.d.) samples. The paper presents mean-squared error and concentration bounds for the classic sample average approximation (SAA) of OCE, assuming sub-Gaussianity. Additionally, an efficient stochastic approximation-based OCE estimator is analyzed, with finite sample bounds derived. To demonstrate practical applications, a risk-aware bandit problem is considered, where OCE serves as the risk metric, and a bound on mis-identification probability is established. Numerical experiments validate the theoretical findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to figure out how much uncertainty is involved in making decisions based on random events. That’s what this paper tries to solve! It looks at ways to estimate “Optimized Certainty Equivalent” risk, which measures how uncertain our decisions are. The researchers create new methods for calculating this risk and show that they work well using mathematical proofs and computer simulations. This helps us understand decision-making in situations where things don’t always go as planned. |
Keywords
» Artificial intelligence » Probability
