ACM International Conference on Knowledge Discovery and Data Mining

KDD

CORE rank

CORE rank (raw)

Acceptance rate

20.0% (2024 research track)

Fields of research

Data Management and Data Science · Machine Learning

Papers indexed

8,232

1994–2026

Best Machine Learning conferences Best Data Science and Information Retrieval conferences

Papers per year

1994844 peak2026

Most published authors

KDD papers

8,232 records sourced from DBLP. Search titles, filter by year, sort by recency.

Year	Title	Authors
2020	Mining Large Quasi-cliques with Quality Guarantees from Vertex Neighborhoods.	Aritra Konar, Nicholas D. Sidiropoulos
2020	Incremental Lossless Graph Summarization.	Jihoon Ko, Yunbum Kook, Kijung Shin
2020	DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection.	Sundong Kim, Yu-Che Tsai, Karandeep Singh, Yeonsoo Choi, Etim Ibok, Cheng-Te Li, Meeyoung Cha
2020	Hi-COVIDNet: Deep Learning Approach to Predict Inbound COVID-19 Patients and Case Study in South Korea.	Minseok Kim, Junhyeok Kang, Doyoung Kim, Hwanjun Song, Hyangsuk Min, Youngeun Nam, Dongmin Park, Jae-Gil Lee
2020	Toward Domain-Guided Controllable Summarization of Privacy Policies.	Moniba Keymanesh, Micha Elsner, Srinivasan Sarthasarathy
2020	Non-Linear Mining of Social Activities in Tensor Streams.	Koki Kawabata, Yasuko Matsubara, Takato Honda, Yasushi Sakurai
2020	Building Continuous Integration Services for Machine Learning.	Bojan Karlas, Matteo Interlandi, Cdric Renggli, Wentao Wu, Ce Zhang, Deepak Mukunthu Iyappan Babu, Jordan Edwards, Chris Lauren, Andy Xu, Markus Weimer
2020	InFoRM: Individual Fairness on Graph Mining.	Jian Kang, Jingrui He, Ross Maciejewski, Hanghang Tong
2020	A Novel Deep Learning Model by Stacking Conditional Restricted Boltzmann Machine and Deep Neural Network.	Tianyu Kang, Ping Chen, John Quackenbush, Wei Ding
2020	Rationale-based Human-in-the-Loop via Supervised Attention.	Teja Kanchinadam, Keith Westpfahl, Qian You, Glenn Fung
2020	Targeted Data-driven Regularization for Out-of-Distribution Generalization.	Mohammad Mahdi Kamani, Sadegh Farhang, Mehrdad Mahdavi, James Z. Wang
2020	Neural Input Search for Large Scale Recommendation Models.	Manas R. Joglekar, Cong Li, Mei Chen, Taibai Xu, Xiaoming Wang, Jay K. Adams, Pranav Khaitan, Jiahui Liu, Quoc V. Le
2020	The Spectral Zoo of Networks: Embedding and Visualizing Networks with Spectral Moments.	Shengmin Jin, Reza Zafarani
2020	An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph.	Jiarui Jin, Jiarui Qin, Yuchen Fang, Kounianhua Du, Weinan Zhang, Yong Yu, Zheng Zhang, Alexander J. Smola
2020	Graph Structure Learning for Robust Graph Neural Networks.	Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, Jiliang Tang
2020	Support for Diverse Students.	Daniel A. Jimnez
2020	Dual Channel Hypergraph Collaborative Filtering.	Shuyi Ji, Yifan Feng, Rongrong Ji, Xibin Zhao, Wanwan Tang, Yue Gao
2020	Personalized Image Retrieval with Sparse Graph Representation Learning.	Xiaowei Jia, Handong Zhao, Zhe Lin, Ajinkya Kale, Vipin Kumar
2020	Redundancy-Free Computation for Graph Neural Networks.	Zhihao Jia, Sina Lin, Rex Ying, Jiaxuan You, Jure Leskovec, Alex Aiken
2020	Residual Correlation in Graph Neural Network Regression.	Junteng Jia, Austin R. Benson
2020	Joint Policy-Value Learning for Recommendation.	Olivier Jeunen, David Rohde, Flavian Vasile, Martin Bompaire
2020	Retrospective Loss: Looking Back to Improve Training of Deep Neural Networks.	Surgan Jandial, Ayush Chopra, Mausoom Sarkar, Piyush Gupta, Balaji Krishnamurthy, Vineeth Balasubramanian
2020	Overview and Importance of Data Quality for Machine Learning Tasks.	Abhinav Jain, Hima Patel, Lokesh Nagalapatti, Nitin Gupta, Sameep Mehta, Shanmukha C. Guttula, Shashank Mujumdar, Shazia Afzal, Ruhi Sharma Mittal, Vitobha Munigala
2020	Improving Task-Oriented Dialogue Systems In Production with Conversation Logs.	Alon Jacovi, Ori Bar El, Ofer Lavi, David Boaz, David Amid, Inbal Ronen, Ateret Anaby-Tavor
2020	The Data Science Mentoring Fire Next Time: Innovative Strategies for Mentoring in Data Science.	Latifa Jackson, Heriberto Acosta Maestre

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