Fast determinantal sampling on general spaces and diffusion geometry
Determinantal point processes have recently emerged as a kernel-based alternative to standard independent sampling for constructing efficient minibatches, coresets, and other compact representations of large-scale datasets. In particular, sampling mechanisms based on DPPs are believed to demonstrate better approximation properties compared to classical i.i.d. samplers, even at the scale of the exponent. One of the key strengths of DPP based samplers is that they can be deployed over very general spaces, in contrast to more classical sampling methods beyond i.i.d. which tend to work in very well-structured settings, principally Euclidean spaces. In this work, we establish explicit rate gua...
| Authors | Hoang-Son Tran, Pranav Gupta, Subhroshekhar Ghosh |
|---|---|
| Primary category | stat.ML |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
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