VLDB 2026 Research / reviewers in the wild / expert
Erkka Haapasalo
dblp:180/1623
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2025
0000-0001-8255-5613ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 2 · 2 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Matrix Majorization in Large Samples With Varying Support RestrictionsabstractWe say that a matrixPwith non-negative entries majorizes another such matrixQif there is a stochastic matrixTsuch thatQ=TP. We study matrix majorization in large samples and in the catalytic regime in the case where the columns of the matrices need not have equal support, as has been assumed in earlier works. We focus on two cases: either there are no support restrictions (except for requiring a non-empty intersection for the supports) or the final column dominates the others. Using real-algebraic methods, we identify sufficient and almost necessary conditions for majorization in large samples or when using catalytic states under these support conditions. These conditions are given in terms of multivariate divergences that generalize the Rényi divergences. We notice that varying support conditions dramatically affect the relevant set of divergences. Our results find an application in the theory of catalytic state transformation in quantum thermodynamics. Frits Verhagen, Marco Tomamichel, Erkka Haapasalo |
IEEE Trans. Inf. Theory | 3 |
| 2024 | Matrix Majorization in Large SamplesabstractOne tuple of probability vectors is more informative than another tuple when there exists a single stochastic matrix transforming the probability vectors of the first tuple into the probability vectors of the other. This is called matrix majorization. Solving an open problem raised by Muet al, we show that if certain monotones—namely multivariate extensions of Rényi divergences—are strictly ordered between the two tuples, then for sufficiently largen, there exists a stochastic matrix taking then-fold Kronecker power of each input distribution to then-fold Kronecker power of the corresponding output distribution. The same conditions, with non-strict ordering for the monotones, are also necessary for such matrix majorization in large samples. Our result also gives conditions for the existence of a sequence of statistical maps that asymptotically (with vanishing error) convert a single copy of each input distribution to the corresponding output distribution with the help of a catalyst that is returned unchanged. Allowing for transformation with arbitrarily small error, we find conditions that are both necessary and sufficient for such catalytic matrix majorization. We derive our results by building on a general algebraic theory of preordered semirings recently developed by one of the authors. This also allows us to recover various existing results on majorization in large samples and in the catalytic regime as well as relative majorization in a unified manner. Muhammad Usman Farooq, Tobias Fritz, Erkka Haapasalo, Marco Tomamichel |
IEEE Trans. Inf. Theory | 3 |