SAN DIEGO, April 23, 2020 /PRNewswire/ — The Association for Computing Machinery’s Special Interest Group on Knowledge Discovery and Data Mining officially opened registration for its annual KDD Cup, the organization’s signature data science competition. This year’s competition features four distinct tracks that welcome participants to tackle challenges in e-commerce, generative adversarial networks, automatic graph representation learning (AutoGraph) and mobility-on-demand (MoD) platforms. Winners will be recognized at KDD 2020, the leading interdisciplinary conference in data science, in San Diego on August 23-27, 2020.
“As one of the first competitions of its kind, the KDD Cup has a long history of solving problems by crowd sourcing participation and has given rise to many other popular competition platforms,” said Iryna Skrypnyk, co-chair of KDD Cup 2020 and head of the AI Innovation Lab at Pfizer. “Today, KDD Cup is not only an opportunity for data scientists to build their profiles and connect with leading companies but apply their skillset to emerging areas with machine learning on graphs like knowledge graph or drug design, and growth markets like the rideshare industry.”
In 2019, more than 2,800 teams registered for the KDD Cup, representing 39 countries and 230 academic or corporate institutions. KDD Cup competition winners are selected by an entirely automated process. In 2020, the KDD Cup features different types of data science including regular machine learning, automated machine learning and reinforcement learning. The competition tracks include:
- “Challenges for Modern E-Commerce Platform” Regular Machine Learning Competition Track 1—Participants will design a model framework to facilitate a sematic understanding, search and retrieval of images or videos, then apply learned representation to compute a similarity score. This track is sponsored by Alibaba, Alibaba DAMO Academy, Duke University, Tsinghua University and University of Illinois at Urbana-Champaign, and offers a total reward of $40,000.
- “Adversarial Attacks and Defense on Academic Graphs” Regular Machine Learning Competition Track 2—Teams will submit a modified version of the provided graph data that will serve as a form of attack and a defender dataset. This track is sponsored by BienData and offers a total reward of $19,000.
- “AutoML for Graph Representation Learning” Automated Machine Learning Competition Track—Participants will deploy an automated machine learning solution for graph representation learning, where node classification is chosen to evaluate the quality of learned representations. This track is sponsored by 4Paradigm, ChaLearn, Stanford University and Google, and offers a total reward of $33,000.
- “Learning to Dispatch and Reposition on a Mobility-on-Demand Platform” Reinforcement Learning Competition Track—Participants are challenged to leverage machine learning tactics to determine a novel solution for order dispatching and vehicle reposition on a Mobility-on-Demand (MoD) platform. This track is sponsored by DiDi Chuxing and offers a total reward of $30,000.
In addition to Iryna Skrypnyk, KDD Cup 2020 is co-chaired by Claudia Perlich, senior data scientist at Two Sigma; Jie Tang, professor of Computer Science at Tsinghua University; and Jieping Ye, vice president of research at Didi Chuxing and associate professor of Computer Science at the University of Michigan. For updates on this year’s KDD Cup and links to each challenge, please visit: www.kdd.org.
About ACM SIGKDD:
ACM is the premier global professional organization for researchers and professionals dedicated to the advancement of the science and practice of knowledge discovery and data mining. SIGKDD is ACM’s Special Interest Group on Knowledge Discovery and Data Mining. The annual KDD International Conference on Knowledge Discovery and Data Mining is the premier interdisciplinary conference for data mining, data science and analytics.
Contact or Follow SIGKDD on:
Facebook—https://www.facebook.com/SIGKDD
Twitter—https://twitter.com/kdd_news
LinkedIn—https://www.linkedin.com/groups/160888/
SOURCE ACM SIGKDD
Related Links
http://www.kdd.org
Credit: Google News
