Summary of No-regret Algorithms For Safe Bayesian Optimization with Monotonicity Constraints, by Arpan Losalka et al.
No-Regret Algorithms for Safe Bayesian Optimization with Monotonicity Constraints
by Arpan Losalka, Jonathan Scarlett
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 addresses the problem of maximizing an unknown function f over a set of actions subject to a safety constraint, where both f and g lie in a reproducing kernel Hilbert space (RKHS). The authors propose a new algorithm that can achieve sublinear regret for sequentially selecting safe actions. The key challenge lies in expanding the safe region without incurring high regret. By assuming monotonicity of g with respect to s, the proposed algorithm achieves sublinear regret. Additionally, a modified version of the algorithm is shown to attain sublinear regret for finding near-optimal solutions for every x. Empirical evaluations on various objective and safety functions support the authors’ findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us solve a tricky problem in machine learning. Imagine you have to choose actions that make sure an unknown function stays safe, while also trying to maximize its value. The new algorithm can find good solutions without getting stuck or making too many mistakes. It works by assuming some special properties about the safety function and the objective function. This is important because it helps us understand how to balance exploring for better solutions with staying within the safe boundaries. |
Keywords
» Artificial intelligence » Machine learning » Objective function
