Trustworthy tree-based machine learning by MoS

Bo Wen, Guoyun Gao, Zhicheng Xu, Mingrui Jiang, Ruibin Mao, Xiaojuan Qi, Jiezhi Chen, Xunzhao Yin, X Sharon Hu, Can Li

Abstract read

In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Bo Wen *Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong SAR, China.ORCID http://orcid.org/0000-0002-6106-9509

Guoyun Gao *Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong SAR, China.ORCID http://orcid.org/0000-0003-2572-1413

Zhicheng XuDepartment of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong SAR, China.ORCID http://orcid.org/0009-0001-2462-0162

Mingrui JiangDepartment of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong SAR, China.ORCID http://orcid.org/0009-0000-2364-3904

Ruibin MaoDepartment of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong SAR, China.ORCID http://orcid.org/0000-0001-6085-0486

Xiaojuan QiDepartment of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong SAR, China.ORCID http://orcid.org/0000-0002-4285-1626

Jiezhi ChenSchool of Information Science and Engineering, Shandong University, Qingdao, China.

Xunzhao YinCollege of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China.ORCID http://orcid.org/0000-0003-4656-9545

X Sharon HuDepartment of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA.ORCID http://orcid.org/0000-0002-6636-9738

Can LiDepartment of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong SAR, China. canl@hku.hk.ORCID http://orcid.org/0000-0003-3795-2008

Funding

Croucher Foundation Croucher Innovation AwardInnovation and Technology Commission (ITF) MHP/363/24Research Grants Council, University Grants Committee (RGC, UGC) 17207925, C7003-24Y, C1009-22GF, T45-701/22-R, C500124Y

6 · The paper itself

Abstract

The rapid advancement of artificial intelligence has raised concerns regarding its trustworthiness, especially in terms of interpretability and robustness. Tree-based models such as Random Forest excel in interpretability and accuracy for tabular data, but scaling them remains computationally expensive due to poor data locality and high data dependence. Previous efforts to accelerate these models with analog content-addressable memory (CAM) have struggled because difficult-to-implement sharp decision boundaries are highly susceptible to device variations, leading to poor hardware performance and vulnerability to adversarial attacks. Here, we present a hardware-software co-design approach using MoS

Identifiers

PMID42031751

DOI10.1038/s41467-026-72118-z

PMCPMC13319242

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.