KDD 2020 : International Conference on Knowledge discovery and data mining

The annual KDD conference is the premier interdisciplinary conference bringing together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.

We invite submission of papers describing innovative research on all aspects of knowledge discovery and data mining, ranging from theoretical foundations to novel models and algorithms for data mining problems in science, business, medicine, and engineering. Visionary papers on new and emerging topics are also welcome, as are application-oriented papers that make innovative technical contributions to research. Authors are explicitly discouraged from submitting incremental results that do not provide major advances over existing approaches.

All deadlines are at 11:59PM Alofi Time. There will be absolutely no exception to these deadlines.

Topics of interest include, but are not limited to:

• Data Science: Methods for analyzing scientific and business data, social networks, time series; mining sequences, streams, text, web, graphs, rules, patterns, logs data, IoT data, spatio-temporal data, biological data; recommender systems, computational advertising, multimedia, finance, bioinformatics.
• Big Data: Large-scale systems for text and graph analysis, machine learning, optimization, sampling, parallel and distributed data mining (cloud, map-reduce, federated learning), novel algorithmic and statistical techniques for big data.
• Foundations: Models and algorithms, asymptotic analysis; model selection, dimensionality reduction, relational/structured learning, matrix and tensor methods, probabilistic and statistical methods; deep learning, meta learning, AutoML, reinforcement learning; classification, clustering, regression, semi-supervised and unsupervised learning; personalization, security and privacy, visualization; fairness, interpretability and robustness.