On the Power of Learning-Augmented Search Trees
learning-augmented; binary search tree; algorithm with predictions
Margin violation
Submission Desk Rejected by Program Chairs
We develop robust learning-augmented BSTs and B-trees that achieve near-optimal performance for arbitrary input.
@misc{
cao2024on,
title={On the Power of Learning-Augmented Search Trees},
author={Xinyuan Cao and Jingbang Chen and Alicia Stepin and Li Chen},
year={2024},
url={https://openreview.net/forum?id=FUwWdUi55e}
}
We study learning-augmented binary search trees (BSTs) via Treaps with carefully designed priorities.
The result is a simple search tree in which the depth of each item $x$ is determined by its predicted weight $w_x$.
Specifically, each item $x$ is assigned a composite priority of $-\lfloor\log\log(1/w_x)\rfloor + U(0, 1)$ where $U(0, 1)$ is the uniform random variable. By choosing $w_x$ as the relative frequency of $x$, the resulting search trees achieve static optimality.
This approach generalizes the recent learning-augmented BSTs [Lin-Luo-Woodruff ICML`22], which only work for Zipfian distributions, by extending them to arbitrary input distributions.
Furthermore, we demonstrate that our method can be generalized to a B-Tree data structure using the B-Treap approach [Golovin ICALP'09]. Our search trees are also capable of leveraging localities in the access sequence through online self-reorganization, thereby achieving the working-set property. Additionally, they are robust to prediction errors and support dynamic operations, such as insertions, deletions, and prediction updates. We complement our analysis with an empirical study, demonstrating that our method outperforms prior work and classic data structures.
I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
~Xinyuan_Cao1 ~Jingbang_Chen1 ~Alicia_Stepin1 ~Li_Chen21
Xinyuan Cao Jingbang Chen Alicia Stepin Li Chen
I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
learning-augmented; binary search tree; algorithm with predictions
I certify that there is no acknowledgement section in this submission for double blind review.
cao|on_the_power_of_learningaugmented_search_trees
/pdf/feabb8500cfb3acb6b03374a42b830863f8fecde.pdf
other topics in machine learning (i.e., none of the above)
I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.
I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
/attachment/ec96193f21dfac56967d67034f8fd5c34e5d37bf.zip
On the Power of Learning-Augmented Search Trees
ICLR 2025 Conference Desk Rejected Submission
ICLR.cc/2025/Conference/Desk_Rejected_Submission