Prof-K: Probabilistic One-Pass Filtering for Efficient Top-k Selection
Top-k selection is a fundamental computational primitive with applications spanning databases, information retrieval, signal processing, and modern machine learning workloads, including sparse activations and attention pruning. As data sizes grow, existing approaches become inefficient: exact methods incur high memory and compute overhead, while approximate methods often rely on brittle heuristics that degrade under adversarial or heavy-tailed inputs. In this paper, we introduce Prof-K, a fast, scalable, and distribution-agnostic algorithm for exact top-k selection. Prof-K performs a single-pass filtering procedure: a small random sample estimates an adaptive threshold, the N input elemen...
| Authors | Tadeusz Dziarmaga, Witold Sikora, Łukasz Struski et al. |
|---|---|
| Primary category | cs.LG |
| Published | 12 August 2026 |
| Last updated at source | 30 September 2026 |
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