The subset selection problem is fundamental in machine learning and other fields of computer science. We introduce a stochastic formulation for the minimum cost subset selection problem in a black box setting, in which only the subset metric value is available. Subsequently, we can handle two-stage schemes, with an outer subset-selection component and an inner subset cost evaluation component. We propose formulating the subset selection problem in a stochastic manner by choosing subsets at random from a distribution whose parameters are learned. Two stochastic formulations are proposed. The first explicitly restricts the subset's cardinality, and the second yields the desired cardinality in expectation. The distribution is parameterized by a decision variable, which we optimize using Stochastic Mirror Descent. Our choice of distributions yields constructive closed-form unbiased stochastic gradient formulas and convergence guarantees, including a rate with favorable dependency on the problem parameters. Empirical evaluation of selecting a subset of layers in transfer learning complements our theoretical findings and demonstrates the potential benefits of our approach.

Extended Berkeley Packet Filter (eBPF) is a Linux subsystem that allows safely executing untrusted user-defined extensions inside the kernel. It relies on static analysis to protect the kernel against buggy and malicious extensions. As the eBPF ecosystem evolves to support more complex and diverse extensions, the limitations of its current verifier, including high rate of false positives, poor scalability, and lack of support for loops, have become a major barrier for developers.