Summary of Testing the Feasibility Of Linear Programs with Bandit Feedback, by Aditya Gangrade et al.
Testing the Feasibility of Linear Programs with Bandit Feedback
by Aditya Gangrade, Aditya Gopalan, Venkatesh Saligrama, Clayton Scott
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistics Theory (math.ST); 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 In this paper, researchers tackle a crucial problem in constrained bandit optimization. They focus on testing feasibility assumptions for linear programs using bandit feedback. To do so, they propose a novel test based on low-regret algorithms and a non-asymptotic law of iterated logarithms. The authors demonstrate that their test is reliable, adapting to the signal level of any instance with mean sample costs scaling as O(d2/Γ2). They also establish a minimax lower bound of Ω(d/Γ^2) for sample costs of reliable tests, which dominates prior asymptotic lower bounds by capturing the dependence on d. This work provides valuable insights into the feasibility testing problem and has implications for constrained bandit optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about solving a tricky problem in computer science called “constrained bandit optimization.” Imagine you’re trying to find the best way to do something, but there are rules you have to follow. The researchers came up with a new way to test if these rules can be followed. They used special algorithms and math to make sure their method works well and doesn’t use too much computer power. This is important because it helps us understand how to solve similar problems in the future. |
Keywords
» Artificial intelligence » Optimization
