Fair Multi-View Determinantal Coresets via Adaptive NEPv
Selecting a small, diverse subset from a large candidate pool often means balancing several incompatible notions of diversity. In trademark curation, for instance, a subset should cover both the language used to describe marks and the visual space of their logos. A single determinantal point process (\DPP) kernel can hide failure in one view, and averaging kernels replaces the multi-view relaxation by an ordinary single-kernel spectral problem. We formulate \emph{fair multi-view determinant selection}: maximize the weakest per-view log determinant of a size-$k$ subset. We smooth this nonsmooth objective and relax it to the Stiefel manifold. The relaxation embeds every discrete subset exac...
| Authors | Richard Yi Da Xu |
|---|---|
| Primary category | stat.ML |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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