English(EN) Randomized Algorithms for Learning Partitions with Near Optimal Query Complexity in Constant Rounds

随机算法大幅降低分区学习查询复杂度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-04 04:00

研究人员开发了新的随机算法用于学习分区，与现有确定性方法相比，显著提高了查询复杂度。这些算法在常数轮次内实现了近乎最优的查询复杂度，是比顺序方法的一项显著进步。该研究表明，随机化极大地改变了分区学习所需的轮次和查询之间的权衡，尤其是在未知部分数量的情况下。 AI

影响改进了对分区学习的理论理解，可能影响相关领域未来算法的设计。

排序理由该集群包含一篇关于计算学习理论算法进展的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Deeparnab Chakrabarty, Aditi Dudeja, David Saulpic · 2026-08-04 04:00

用于在恒定轮次中学习分区且查询复杂度接近最优的随机算法

arXiv:2608.02176v1 Announce Type: cross Abstract: We study the round complexity of learning a hidden partition $\mathcal{P}$ of an $n$-element universe using PAIR queries: PAIR($x,y$) tells us whether $x$ and $y$ belong to the same part of the partition or not. While it is easy t…