KDD

Towards Fair Disentangled Online Learning for Changing Environments

Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Christan Grant, Feng Chen

Comments Accepted by KDD 2023

kHGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learning

Menglin Yang, Min Zhou, Lujia Pan, Irwin King

Comments KDD 2023

Sketch-Based Anomaly Detection in Streaming Graphs

Siddharth Bhatia, Mohit Wadhwa, Kenji Kawaguchi, Neil Shah, Philip S. Yu, Bryan Hooi

Comments Accepted at SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023

A Study of Situational Reasoning for Traffic Understanding

Jiarui Zhang, Filip Ilievski, Kaixin Ma, Aravinda Kollaa, Jonathan Francis, Alessandro Oltramari

Comments 11 pages, 6 figures, 5 tables, camera ready version of SIGKDD 2023

Adaptive Graph Contrastive Learning for Recommendation

Yangqin Jiang, Chao Huang, Lianghao Xia

Comments This paper is accepted by KDD'23

Leveraging covariate adjustments at scale in online A/B testing

Lorenzo Masoero, Doug Hains, James McQueen

Journal ref 2023 ACM SIGKDD Workshop on Causal Discovery, Prediction and Decision

Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling

Bei Yang, Jie Gu, Ke Liu, Xiaoxiao Xu, Renjun Xu, Qinghui Sun, Hong Liu

Comments Accepted by KDD 2023

Learning Behavioral Representations of Routines From Large-scale Unlabeled Wearable Time-series Data Streams using Hawkes Point Process

Tiantian Feng, Brandon M Booth, Shrikanth Narayanan

Comments 2023 9th ACM SIGKDD International Workshop on Mining and Learning From Time Series (MiLeTS 2023)

Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey

Dongqi Fu, Wenxuan Bao, Ross Maciejewski, Hanghang Tong, Jingrui He

Comments Accepted by SIGKDD Explorations 2023, Volume 25, Issue 1

Generalizing Graph ODE for Learning Complex System Dynamics across Environments

Zijie Huang, Yizhou Sun, Wei Wang

Journal ref KDD 2023

Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance

Yuchen Fang, Zhenggang Tang, Kan Ren, Weiqing Liu, Li Zhao, Jiang Bian, Dongsheng Li, Weinan Zhang, Yong Yu, Tie-Yan Liu

Comments Accepted in KDD 2023; The website is at https://seqml.github.io/marl4fin

Finding Favourite Tuples on Data Streams with Provably Few Comparisons

Guangyi Zhang, Nikolaj Tatti, Aristides Gionis

Comments To appear in KDD 2023

LEA: Improving Sentence Similarity Robustness to Typos Using Lexical Attention Bias

Mario Almagro, Emilio Almazán, Diego Ortego, David Jiménez

Comments KDD'23 conference (main research track). (*) These authors contributed equally

Knowledge Graph Self-Supervised Rationalization for Recommendation

Yuhao Yang, Chao Huang, Lianghao Xia, Chunzhen Huang

Comments Accepted by KDD'23

Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning

Daeun Lee, Sejung Son, Hyolim Jeon, Seungbae Kim, Jinyoung Han

Comments KDD 2023 accepted

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

Neuro-Symbolic Sudoku Solver

Ashutosh Hathidara, Lalit Pandey

Comments Published as a conference paper at KDD KiML 2023

DECOR: Degree-Corrected Social Graph Refinement for Fake News Detection

Jiaying Wu, Bryan Hooi

Comments Accepted to KDD 2023 (Research Track)

Federated Few-shot Learning

Song Wang, Xingbo Fu, Kaize Ding, Chen Chen, Huiyuan Chen, Jundong Li

Comments SIGKDD 2023

Impact-Oriented Contextual Scholar Profiling using Self-Citation Graphs

Yuankai Luo, Lei Shi, Mufan Xu, Yuwen Ji, Fengli Xiao, Chunming Hu, Zhiguang Shan

Comments KDD 2023

Contrastive Meta-Learning for Few-shot Node Classification

Song Wang, Zhen Tan, Huan Liu, Jundong Li

Comments SIGKDD 2023

Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach

Zhangming Chan, Yu Zhang, Shuguang Han, Yong Bai, Xiang-Rong Sheng, Siyuan Lou, Jiacen Hu, Baolin Liu, Yuning Jiang, Jian Xu, Bo Zheng

Comments Accepted at KDD 2023. This work has already been deployed on the display advertising system in Alibaba, bringing substantial economic gains

TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou

Jianxin Chang, Chenbin Zhang, Zhiyi Fu, Xiaoxue Zang, Lin Guan, Jing Lu, Yiqun Hui, Dewei Leng, Yanan Niu, Yang Song, Kun Gai

Comments Accepted by KDD 2023

PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information

Jianxin Chang, Chenbin Zhang, Yiqun Hui, Dewei Leng, Yanan Niu, Yang Song, Kun Gai

Comments Accepted by KDD 2023

Anomaly Detection with Score Distribution Discrimination

Minqi Jiang, Songqiao Han, Hailiang Huang

Comments Accepted by KDD 2023. Detailed discussions can be found in https://openreview.net/forum?id=P1Worw-M1Tf&referrer=[the%20profile%20of%20Minqi%20Jiang](/profile?id=~Minqi_Jiang2)

Constraint-aware and Ranking-distilled Token Pruning for Efficient Transformer Inference

Junyan Li, Li Lyna Zhang, Jiahang Xu, Yujing Wang, Shaoguang Yan, Yunqing Xia, Yuqing Yang, Ting Cao, Hao Sun, Weiwei Deng, Qi Zhang, Mao Yang

Comments KDD 2023

Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks

Gaotang Li, Marlena Duda, Xiang Zhang, Danai Koutra, Yujun Yan

Comments To appear in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 23)

Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training

Fan Liu, Weijia Zhang, Hao Liu

Comments Accepted by KDD 2023

Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders

Dingsu Wang, Yuchen Yan, Ruizhong Qiu, Yada Zhu, Kaiyu Guan, Andrew J Margenot, Hanghang Tong

Comments KDD 2023

IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representations

Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Manas Gaur, Amit Sheth

Comments Accepted for publication at the KDD workshop on Knowledge-infused Machine Learning, 2023

Similarity Preserving Adversarial Graph Contrastive Learning

Yeonjun In, Kanghoon Yoon, Chanyoung Park

Comments 9 pages; KDD'23