Summary of Fair Secretaries with Unfair Predictions, by Eric Balkanski et al.
Fair Secretaries with Unfair Predictions
by Eric Balkanski, Will Ma, Andreas Maggiori
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS)
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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 presents an innovative framework for decision-making under uncertainty, leveraging machine-learned predictions without assuming their quality. The goal is to achieve improved performance when predictions are accurate while maintaining acceptable guarantees when they’re erroneous. The framework’s concern is bias in predictions, leading to unfair decisions. In the classical secretary problem, the state-of-the-art algorithm can have zero probability of accepting the best candidate, deemed unfair despite promising to accept a candidate with expected value at least 1-epsilon times optimal. This paper shows how to preserve this promise while guaranteeing to accept the best candidate with probability Omega(1). The “pegging” idea unifies and simplifies existing results, and is extended to the k-secretary problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way for computers to make decisions when they’re not sure what’s going to happen. It uses information that the computer has learned from data to help it make better choices. The problem is that sometimes this information can be wrong, which means the computer might make bad decisions. The researchers want to find a way to make good decisions even when the computer is unsure. They use an example called the “secretary problem” to show that current methods don’t always work well. They then introduce a new idea called “pegging” that helps solve this problem and makes sure the computer will choose the best option with some certainty. |
Keywords
» Artificial intelligence » Probability
