This paper studies the problem of quantifying dataset hardness for learned indexes and proposes two scores, conformance and coverage, that together characterize metric quality while capturing a fundamental tradeoff between metric accuracy and applicability.
This work proposes differentially private algorithms for MSD under both cardinality and matroid constraints, achieving nearly optimal utility guarantees and design more efficient algorithms that maintain strong guarantees.
Ron Zadicario, Tova Milo· Proceedings of the VLDB Endo...· 1 citation
LowRankArena is presented, a standardized evaluation platform for SVD-based LLM compression that unifies task versions, uniform-precision compression budgets, comparison regimes, and inference measurements, and provides a reproducible pipeline with over 3 TiB released compressed checkpoints.
Zishan Shao, Li-Xun Zhang, Kangning Cui et al.· 0 citations
Learned index structures use machine learning models, rather than traditional algorithmic data structures, to optimize database indexing performance. This systematic literature review applies the PICOC framework to 49 peer-reviewed articles (2017–2025) on learned index effectiveness versus traditional methods, using sy...
Manar Abdelhamid, D. Elzanfaly, K. Nagaty et al.· Journal of Big Data· 0 citations
This work argues that benchmark evaluation should also consider the data-centric peak performance frontier, defined by the best statistically supported performance achieved on each dataset, and finds that common aggregation metrics are highly correlated and largely measure consistency and avoiding failures, while being...
Andrej Tschalzev, Stefan Lüdtke, Heiner Stuckenschmidt et al.· 0 citations
This work asks what a compression statistic can justify when deployment cares about the worst supplied group, and treats each statistic as an information interface.
Real distance data rarely cooperate: measurements are noisy, observations are missing, and the numbers that result seldom satisfy the triangle inequality. A family of methods exists to correct them, and every one of those methods rests on the same hope --- that analysis run on the corrected data is a more faithful surr...
Asaf Etgar, A. Gilbert· 0 citations
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