Determinantal Point Process

Determinantal Point Processes (DPPs) are probabilistic models that generate diverse subsets of items, exhibiting negative correlation—a repulsion between selected elements. Current research focuses on efficient algorithms for DPP sampling and MAP inference, particularly for high-dimensional data and large-scale applications, often employing greedy algorithms or leveraging low-rank kernel approximations. DPPs are proving valuable in diverse fields, enhancing retrieval-augmented generation, improving in-context learning by selecting informative examples, and optimizing data sampling for machine learning tasks like image modeling and reinforcement learning. Their ability to model diversity and negative dependence makes them a powerful tool for various applications requiring subset selection and data diversification.