KDD 2020 : KDD 2020

  in Conferences   Posted on December 6, 2019

Conference Information

Submission Deadline Thursday 13 Feb 2020 Proceedings indexed by :
Conference Dates Aug 22, 2020 - Aug 27, 2020
Conference Address San Diego, United States
Conference & Submission Link https://www.kdd.org/kdd2020/
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Call for Papers

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.

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