Discrete and Continuous Submodular Bandits with Full Bandit Feedback
Vaneet Aggarwal from Purdue University presented new research on discrete and continuous submodular bandits with full bandit feedback. The research introduces a framework transforming discrete offline approximation algorithms into sublinear α-regret methods using bandit feedback. Additionally, it introduces a unified approach for maximizing continuous DR-submodular functions, accommodating various settings and oracle access types. Why it matters: This research provides new methods for optimization under uncertainty, which is crucial for real-world AI applications in the region, such as resource allocation and automated decision-making.