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
2021	Preface: The 2021 ACM SIGKDD Workshop on Causal Discovery.	Thuc Duy Le, Jiuyong Li, Gregory F. Cooper, Sofia Triantafyllou, Elias Bareinboim, Huan Liu, Negar Kiyavash
2021	Fast Rotation Kernel Density Estimation over Data Streams.	Runze Lei, Pinghui Wang, Rundong Li, Peng Jia, Junzhou Zhao, Xiaohong Guan, Chao Deng
2021	SEMI: A Sequential Multi-Modal Information Transfer Network for E-Commerce Micro-Video Recommendations.	Chenyi Lei, Yong Liu, Lingzi Zhang, Guoxin Wang, Haihong Tang, Houqiang Li, Chunyan Miao
2021	Dip-based Deep Embedded Clustering with k-Estimation.	Collin Leiber, Lena G. M. Bauer, Benjamin Schelling, Christian Bhm, Claudia Plant
2021	Diet Planning with Machine Learning: Teacher-forced REINFORCE for Composition Compliance with Nutrition Enhancement.	Changhun Lee, Soohyeok Kim, Chiehyeon Lim, Jayun Kim, Yeji Kim, Minyoung Jung
2021	Architecture and Operation Adaptive Network for Online Recommendations.	Lang Lang, Zhenlong Zhu, Xuanye Liu, Jianxin Zhao, Jixing Xu, Minghui Shan
2021	Micro-climate Prediction - Multi Scale Encoder-decoder based Deep Learning Framework.	Peeyush Kumar, Ranveer Chandra, Chetan Bansal, Shivkumar Kalyanaraman, Tanuja Ganu, Michael Grant
2021	Machine Learning in Finance.	Senthil Kumar, Leman Akoglu, Nitesh V. Chawla, Jos A. Rodrguez-Serrano, Tanveer A. Faruquie, Saurabh Nagrecha
2021	2021 KDD Workshop on Understanding Public Perceptions for Applied Data Science: How Important is it to Engage Society in Technology Development?	ChanGhee Koh, SoYoung Kim, Nathaniel Tan
2021	A Color-blind 3-Approximation for Chromatic Correlation Clustering and Improved Heuristics.	Nicolas Klodt, Lars Seifert, Arthur Zahn, Katrin Casel, Davis Issac, Tobias Friedrich
2021	Estimating individual-level optimal causal interventions combining causal models and machine learning models.	Keisuke Kiritoshi, Tomonori Izumitani, Kazuki Koyama, Tomomi Okawachi, Keisuke Asahara, Shohei Shimizu
2021	Q-Learning Lagrange Policies for Multi-Action Restless Bandits.	Jackson A. Killian, Arpita Biswas, Sanket Shah, Milind Tambe
2021	Auditing for Diversity Using Representative Examples.	Vijay Keswani, L. Elisa Celis
2021	Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning.	Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna M. Wallach, Jennifer Wortman Vaughan
2021	Addressing Non-Representative Surveys using Multiple Instance Learning.	Yaniv Katz, Oded Vainas
2021	Workshop on Data-Efficient Machine Learning (DeMaL).	Sumeet Katariya, Nikhil Rao, Chandan K. Reddy
2021	Network Experimentation at Scale.	Brian Karrer, Liang Shi, Monica Bhole, Matt Goldman, Tyrone Palmer, Charlie Gelman, Mikael Konutgan, Feng Sun
2021	Joint Graph Embedding and Alignment with Spectral Pivot.	Paris A. Karakasis, Aritra Konar, Nicholas D. Sidiropoulos
2021	Topology Distillation for Recommender System.	SeongKu Kang, Junyoung Hwang, Wonbin Kweon, Hwanjo Yu
2021	Learning to Embed Categorical Features without Embedding Tables for Recommendation.	Wang-Cheng Kang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Ting Chen, Lichan Hong, Ed H. Chi
2021	Preference Amplification in Recommender Systems.	Dimitris Kalimeris, Smriti Bhagat, Shankar Kalyanaraman, Udi Weinsberg
2021	A Hyper-surface Arrangement Model of Ranking Distributions.	Shizuo Kaji, Akira Horiguchi, Takuro Abe, Yohsuke Watanabe
2021	Learning to Walk across Time for Interpretable Temporal Knowledge Graph Completion.	Jaehun Jung, Jinhong Jung, U Kang
2021	Graph Representation Learning: Foundations, Methods, Applications and Systems.	Wei Jin, Yao Ma, Yiqi Wang, Xiaorui Liu, Jiliang Tang, Yukuo Cen, Jiezhong Qiu, Jie Tang, Chuan Shi, Yanfang Ye, Jiawei Zhang, Philip S. Yu
2021	Towards a Better Understanding of Linear Models for Recommendation.	Ruoming Jin, Dong Li, Jing Gao, Zhi Liu, Li Chen, Yang Zhou

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