Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies

Shlomi Weitzman, Sivan Sabato

Research output: Contribution to journalConference articlepeer-review

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 include both maximization under a cardinality constraint and a minimum cost coverage guarantee. In addition, we provided an approximation guarantee for a modified prior, which is crucial for obtaining active learning guarantees that do not depend on the smallest probability in the prior. Moreover, we discover a new parameter of adaptive selection policies, which we term the maximal gain ratio. We show that this parameter is strictly less restrictive than the greedy approximation parameter that has been used in previous approximation guarantees, and show that it can be used to provide stronger approximation guarantees than previous results. In particular, we show that the maximal gain ratio is never larger than the greedy approximation factor of a policy, and that it can be considerably smaller. This provides a new insight into the properties that make a policy useful for adaptive combinatorial maximization.

Original languageEnglish
Pages (from-to)1188-1207
Number of pages20
JournalProceedings of Machine Learning Research
Volume237
StatePublished - 1 Jan 2024
Event35th International Conference on Algorithmic Learning Theory, ALT 2024 - La Jolla, United States
Duration: 25 Feb 202428 Feb 2024

Keywords

  • Combinatorial maximization
  • adaptive submodularity
  • approximate greedy policies

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

Fingerprint

Dive into the research topics of 'Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies'. Together they form a unique fingerprint.

Cite this