Determinantal point processes (DPPs) have become prominent in data summarization and recommender system tasks for their ability to simultaneously model diversity as well as relevance. In practical applications, k-Determinantal point processes (k-DPPs) are used to yield a selection of k items from a set of size N that are the most representative of the set. In this paper, we study a special case of the diverse subset selection problem where a fixed set GO is already given as a forced recommendation and the task is to determine the remainder of the recommendation G1. The standard k-DPP optimization objectives here can suggest items that are close to optimal when considering only items in G1, but are arbitrarily close to items in G0, i.e., they might not be sufficiently diverse w.r.t. G0. We explore a semi-supervised k-DPP objective that simultaneously considers G0 and G1 and compares the difference between the two recommendations. We demonstrate our findings using multiple examples where the diverse subset selection problem with forced recommendation is important in practice.

Recent developments in artificial intelligence and automation support a new drug design paradigm: autonomous drug design. Under this paradigm, generative models can provide suggestions on thousands of molecules with specific properties, and automated laboratories can potentially make, test and analyze molecules with minimal human supervision. However, since still only a limited number of molecules can be synthesized and tested, an obvious challenge is how to efficiently select among provided suggestions in a closed-loop system. We formulate this task as a stochastic multi-armed bandit problem with multiple plays, volatile arms and similarity information. To solve this task, we adapt previous work on multi-armed bandits to this setting, and compare our solution with random sampling, greedy selection and decaying-epsilon-greedy selection strategies. According to our simulation results, our approach has the potential to perform better exploration and exploitation of the chemical space for autonomous drug design.