KDD

A Unified Approach to Interpretable Causal Root Cause Attribution

可解释性因果根因归因的统一方法

Jing Zhou, Dominik Janzing, Sepp Tsang, Patrick Blöbaum, Marco Visentini Scarzanella

AI总结 针对复杂电商系统的指标变化根因归因，提出结合结构因果信息的统一方法，兼顾可解释性、效率与因果有效性，提升了根因归因的准确性。

Comments Accepted by KDD workshop 2026

MedTVL: Harnessing Vision and Language for Medical Time Series Classification

MedTVL：利用视觉与语言进行医学时间序列分类

Jiexia Ye, Jia Li, Fugee Tsung

机构 * The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）） ； The Hong Kong University of Science and Technology（香港科技大学） 纠错

AI总结 该研究提出文本引导双路径架构MedTVL，结合卷积时间路径与Transformer视觉路径，辅以自适应医学文本语义和混合专家机制，支持多模态对比学习，在多医学任务上展现出优越的分类性能与可迁移性。

Comments 12 pages, 10 figures, 7 tables

Journal ref KDD 2026

Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM

Hyunsung Kim, Han-Jun Choi, Chang Jo Kim, Jinsung Yoon, Sang-Ki Ko

Journal ref Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023)

SoccerCPD: Formation and Role Change-Point Detection in Soccer Matches Using Spatiotemporal Tracking Data

Hyunsung Kim, Bit Kim, Dongwook Chung, Jinsung Yoon, Sang-Ki Ko

Journal ref Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022)