Author

Žiga Virk

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Preprint Sep 2026

Statistical Inference for Persistence Diagrams via Landmark Embeddings: Minimax Theory and Finite Approximation

Hilbert-space embeddings enable inference for populations of persistence diagrams, but separation between individual diagrams need not survive population averaging. We develop a framework for inference on population mean embeddings, with particular attention to the additive landmark representations PLACE and PALACE. Tr...

Pramita Bagchi, Sushovan Majhi, Atish Mitra et al. · 4 citations

#machine learning Preprint Sep 2026

COMPLEX: A Closed-Form Certified Embedding of Multiparameter Persistence Modules

Every multiparameter persistence vectorization we know of carries a one-sided Lipschitz upper bound and nothing below it: without a lower gauge there is no sense in which the features are faithful, and no per-prediction guarantee can be built on them. This paper supplies the missing side. COMPLEX is a closed-form, trai...

Sushovan Majhi, Atish Mitra, Žiga Virk et al. · 0 citations