Summary of On Learning For Ambiguous Chance Constrained Problems, by a Ch Madhusudanarao et al.
On Learning for Ambiguous Chance Constrained Problems
by A Ch Madhusudanarao, Rahul Singh
First submitted to arxiv on: 31 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
GrooveSquid.com Paper Summaries
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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 The paper proposes a novel approach to solve ambiguous chance-constrained optimization problems, where the distribution is not known to the decision maker. The goal is to minimize a function subject to a probability constraint, which is satisfied with high probability. The authors show that by drawing samples from a reference distribution, they can obtain an approximate solution to the original problem, and derive the sample complexity required to achieve this approximation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us solve complex problems when we don’t know what might happen in advance. It’s like trying to predict the weather without knowing the exact forecast. By taking many random guesses from a known pattern, we can get close enough to the correct answer. The researchers figure out how many of these guesses we need to make to be sure our answer is good enough. |
Keywords
* Artificial intelligence * Optimization * Probability
