Summary of Online Learning with Unknown Constraints, by Karthik Sridharan and Seung Won Wilson Yoo
Online Learning with Unknown Constraints
by Karthik Sridharan, Seung Won Wilson Yoo
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes a novel approach to online learning where the learner must adhere to an unknown safety constraint at every round. The goal is to minimize regret with respect to the best safe action in hindsight while simultaneously satisfying the safety constraint with high probability on each round. To achieve this, the authors provide a general meta-algorithm that leverages an online regression oracle to estimate the unknown safety constraint and converts the predictions of an online learning oracle to predictions that adhere to the unknown safety constraint. The algorithm’s regret can be bounded by the regret of the online regression and online learning oracles, the eluder dimension of the model class containing the unknown safety constraint, and a novel complexity measure that captures the difficulty of safe learning. The authors also provide an asymptotic lower bound that shows that the aforementioned complexity measure is necessary. When the constraints are linear, they instantiate their result to provide a concrete algorithm with regret using a scaling transformation that balances optimistic exploration with pessimistic constraint satisfaction. The proposed approach has significant implications for online learning in domains where safety is critical, such as autonomous systems and medical diagnosis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn more about how machines can make good decisions while following rules. Imagine you’re playing a game where you need to take actions one at a time, but each action must follow some secret rule. The goal is to minimize the difference between your actions and the best possible actions that could have been taken. At the same time, we want to ensure that our actions always follow this secret rule. To solve this problem, the authors propose a new method that combines two other methods: one for estimating the unknown rule and another for making good decisions based on past experiences. The method is designed to balance exploration (trying out different actions) with constraint satisfaction (following the rules). The authors also show that their approach is necessary by proving a lower bound. This research has important implications for applications where safety is crucial, such as autonomous vehicles or medical diagnosis systems. |
Keywords
* Artificial intelligence * Online learning * Probability * Regression
