Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies

arXiv:2404.01930v1 Announce Type: new
Abstract: We study adaptive combinatorial maximization, which is a core challenge in machine learning, with applications in active learning as well as many other domains. We study the Bayesian setting, and consider the objectives of maximization under a cardinality constraint and minimum cost coverage. We provide new comprehensive approximation guarantees that subsume previous results, as well as considerably strengthen them. Our approximation guarantees simultaneously support the maximal gain ratio as well as near-submodular utility functions, and …

abstract active learning applications approximation arxiv bayesian beyond challenge core cost coverage cs.dm cs.lg domains machine machine learning policies stat.ml study type