State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
Determinantal point processes (DPPs) have emerged as a kernelized alternative to vanilla independent sampling for generating efficient minibatches, coresets and other parsimonious representations of large-scale datasets. While theoretical foundations and promising empirical performance have been demonstrated, there are two challenges for current proposals for DPP-based coresets or minibatches. The first is the need for families of DPPs with certain key variance reduction properties, usually constructed in a continuous setting, of which there are few known examples. The second is the need for an ad-hoc construction of a discrete DPP defined on a given dataset, that inherits such variance r...
| Authors | Hoang-Son Tran, Pranav Gupta, Rémi Bardenet et al. |
|---|---|
| Primary category | stat.ML |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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