Abstract: Sequential information gathering, i.e., selectively acquiring the most useful data, plays a key role in interactive machine learning systems. Such problem has been studied in the context of Bayesian active learning and experimental design, optimal control and numerous other domains. In this talk, we focus on a class of information gathering tasks, where the goal is to learn the value of some unknown target variable through a sequence of informative, possibly noisy tests. In contrast to prior work, we focus on the challenging, yet practically relevant setting where test outcomes can be conditionally dependent given the hidden target variable. Under such assumptions, common heuristics, such as greedily performing tests that maximize the reduction in uncertainty of the target, often perform poorly.
We propose a class of novel, computationally efficient active learning algorithms, and prove strong theoretical guarantees that hold with correlated, possibly noisy tests. Rather than myopically optimize the value of a test (which, in our case, is the expected reduction in prediction error), at each step, our algorithms pick the test that maximizes the gain in a surrogate objective, which is (1) aligned with the active learning task (2) efficient to evaluate and (3) adaptive submodular. This latter property enables us to utilize an efficient greedy optimization while providing strong approximation guarantees. We demonstrate our algorithms in several real-world problem instances, including a touch-based location task on an actual robotic platform, and an active preference learning task via pairwise comparisons.