We present the first learning-augmented data structure for implementing dictionaries with optimal consistency and robustness. Our data structure, named RobustSL, is a skip list augmented by predictions of access frequencies of elements in a data sequence. With proper predictions, RobustSL has optimal consistency (achieves static optimality). At the same time, it maintains a logarithmic running time for each operation, ensuring optimal robustness, even if predictions are generated adversarially. Therefore, RobustSL has all the advantages of the recent learning-augmented data structures of Lin, Luo, and Woodruff (ICML 2022) and Cao et al. (arXiv 2023), while providing robustness guarantees that are absent in the previous work. Numerical experiments show that RobustSL outperforms alternative data structures using both synthetic and real datasets.

翻译：我们提出了首个实现最优一致性和鲁棒性的学习增强字典数据结构。该数据结构名为RobustSL，是一种通过数据序列中元素访问频率预测增强的跳跃表。在预测准确的情况下，RobustSL具有最优一致性（实现静态最优性）。同时，即使预测由对抗方式生成，该结构仍能保证每个操作的对数级别运行时间，确保最优鲁棒性。因此，RobustSL兼具Lin、Luo和Woodruff（ICML 2022）以及Cao等人（arXiv 2023）提出的最新学习增强数据结构的全部优势，同时提供了先前工作中缺失的鲁棒性保证。数值实验表明，在合成数据集与真实数据集上，RobustSL的性能均优于替代数据结构。