activity
20192025
most citedAn interpretable neural network model through piecewise linear approximation

7 citations · 8 across the 3 of their papers we have counts for

collaborators

5 papers

cs.IR2025

HIT Model: A Hierarchical Interaction-Enhanced Two-Tower Model for Pre-Ranking Systems

Haoqiang Yang, Congde Yuan, Kun Bai +3

Online display advertising platforms rely on pre-ranking systems to efficiently filter and prioritize candidate ads from large corpora, balancing relevance to users with strict com…

cs.LG20231 cited

Data-driven Preference Learning Methods for Sorting Problems with Multiple Temporal Criteria

Yijun Li, Mengzhuo Guo, Miłosz Kadziński +1

The advent of predictive methodologies has catalyzed the emergence of data-driven decision support across various domains. However, developing models capable of effectively handlin…

cs.LG20207 cited

An interpretable neural network model through piecewise linear approximation

Mengzhuo Guo, Qingpeng Zhang, Xiuwu Liao +1

Most existing interpretable methods explain a black-box model in a post-hoc manner, which uses simpler models or data analysis techniques to interpret the predictions after the mod…

cs.LG2019

Explainable Ordinal Factorization Model: Deciphering the Effects of Attributes by Piece-wise Linear Approximation

Mengzhuo Guo, Zhongzhi Xu, Qingpeng Zhang +2

Ordinal regression predicts the objects' labels that exhibit a natural ordering, which is important to many managerial problems such as credit scoring and clinical diagnosis. In th…

cs.LG2019

A hybrid machine learning framework for analyzing human decision making through learning preferences

Mengzhuo Guo, Qingpeng Zhang, Xiuwu Liao +2

Machine learning has recently been widely adopted to address the managerial decision making problems, in which the decision maker needs to be able to interpret the contributions of…