Summary of Parameter-free Algorithms For Performative Regret Minimization Under Decision-dependent Distributions, by Sungwoo Park et al.
Parameter-Free Algorithms for Performative Regret Minimization under Decision-Dependent Distributions
by Sungwoo Park, Junyeop Kwon, Byeongnoh Kim, Suhyun Chae, Jeeyong Lee, Dabeen Lee
First submitted to arxiv on: 23 Feb 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 A novel approach to performative risk minimization is presented in this paper, which tackles stochastic optimization under decision-dependent distributions. The authors develop efficient parameter-free optimistic optimization-based methods for non-convex cases, outperforming existing Lipschitz bandit-based methods in several aspects. Unlike previous work, the proposed framework does not rely on knowledge of sensitivity parameters or loss function constants, making it more practical and efficient. Experimental results demonstrate the superiority of the new algorithms over existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make decisions when the outcome depends on what we choose. It’s like trying to find the best way to get a reward while also avoiding risks. The researchers created some new tools that can help us do this more effectively, without needing too much information about the situation. They tested these tools and showed they work better than previous methods. |
Keywords
* Artificial intelligence * Loss function * Optimization
