Summary of Conformal Validity Guarantees Exist For Any Data Distribution (and How to Find Them), by Drew Prinster et al.
Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)
by Drew Prinster, Samuel Stanton, Anqi Liu, Suchi Saria
First submitted to arxiv on: 10 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper introduces a new approach to quantifying and controlling risk in artificial intelligence (AI) and machine learning (ML) systems, particularly those with autonomy to collect data. The authors focus on conformal prediction, which is promising for uncertainty and risk quantification. However, previous variants relied on the assumption of “quasi-exchangeability” on the data distribution, excluding sequential shifts common in AI/ML applications like black-box optimization and active learning. This paper proves that conformal prediction can be extended to any joint data distribution, not just exchangeable or quasi-exchangeable ones. The authors provide a procedure for deriving specific conformal algorithms for any data distribution and apply it to derive tractable algorithms for AI/ML-agent-induced covariate shifts. The proposed algorithms are evaluated empirically on synthetic black-box optimization and active learning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI is getting more powerful, but it also risks making mistakes that can have big consequences. To make sure AI doesn’t go wrong, we need to understand how it makes decisions and how it might change the data it uses. This paper looks at a special kind of math called conformal prediction that helps us predict what will happen when AI makes new decisions. The problem is that this math usually assumes that the data is “exchangeable,” which means that the order in which we collect the data doesn’t matter. But AI often collects data in a way that changes the data, making it not exchangeable anymore. This paper shows how to use conformal prediction with any kind of data and gives some examples of how this might work. |
Keywords
» Artificial intelligence » Active learning » Machine learning » Optimization
