Sivan Sabato, Tom Hess.
Year: 2018, Volume: 18, Issue: 229, Pages: 1−39
We consider interactive algorithms in the pool-based setting, and in the stream-based setting. Interactive algorithms observe suggested elements (representing actions or queries), and interactively select some of them and receive responses. Pool- based algorithms can select elements at any order, while stream- based algorithms observe elements in sequence, and can only select elements immediately after observing them. We further consider an intermediate setting, which we term precognitive stream, in which the algorithm knows in advance the identity of all the elements in the sequence, but can select them only in the order of their appearance. For all settings, we assume that the suggested elements are generated independently from some source distribution, and ask what is the stream size required for emulating a pool algorithm with a given pool size, in the stream-based setting and in the precognitive stream setting. We provide algorithms and matching lower bounds for general pool algorithms, and for utility-based pool algorithms. We further derive nearly matching upper and lower bounds on the gap between the two settings for the special case of active learning for binary classification.