mlsanityData to 7 Oct 2026

Safe Convex Learning under Uncertain Constraints

arXiv 1903.04626 ↗First posted 11 Mar 2019math.OCcited by 17

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Abstract (arXiv)

We address the problem of minimizing a convex smooth function f(x) over a compact polyhedral set D given a stochastic zeroth-order constraint feedback model. This problem arises in safety-critical machine learning applications, such as personalized medicine and robotics. In such cases, one needs to ensure constraints are satisfied while exploring the decision space to find optimum of the loss function. We propose a new variant of the Frank-Wolfe algorithm, which applies to the case of uncertain linear constraints. Using robust optimization, we provide the convergence rate of the algorithm while guaranteeing feasibility of all iterates, with high probability.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Mar 2019.

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Citing papers per year: 2021: 6; 2022: 1; 2023: 5; 2024: 4; 2025: 0; 2026: 1.

17 papers.

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