DLP-KDD 2020

2nd Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD 2020

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Website for dlp-kdd2019 can be found here

Introduction

In the increasingly digitalized world, it is of utmost importance for various applications to harness the ability to process, understand, and exploit data collected from the Internet. For instance, in customer-centric applications such as personalized recommendation, online advertising, and search engines, interest/intention modeling from customers’ behavioral data can not only significantly enhance user experiences but also greatly contribute to revenues. Recently, we have witnessed that Deep Learning-based approaches began to empower these internet- scale applications by better leveraging the massive data. However, the data in these internet-scale applications are high dimensional and extremely sparse, which makes it different from many applications with dense data such as image classification and speech recognition where Deep Learning-based approaches have been extensively studied. For example, the training samples of a typical click-through rate (CTR) prediction task often involve billions of sparse features, how to mine, model and inference from such data becomes an interesting problem, and how to leverage such data in Deep Learning could be a new research direction. The characteristics of such data pose unique challenges to the adoption of Deep Learning in these applications, including modeling, training, and online serving, etc. More and more communities from both academia and industry have initiated the endeavors to solve these challenges. This workshop will provide a venue for both the research and engineering communities to discuss the challenges, opportunities, and new ideas in the practice of Deep Learning on high-dimensional sparse data.

Important Dates

  • Submission deadline: May 20, 2020 June 10, 2020 (We extended the deadline due to current COVID-19 situation)
  • Acceptance notification: June 15, 2020.
  • Workshop date: August 24, 2020 (San Diego Convention Center, San Diego, California USA)

Workshop Format and Schedule

Half day. We would like to hold a half-day workshop in order to have enough new ground to cover invited academic and industry talks with a focus on many deep learning related areas. The tentative program schedule includes the following components:

  • Ten oral presentations from paper submissions for 15 minutes each
  • A poster session if there are lots of submissions for 45 minutes
  • Four invited talks with a mix of industry and academia for 1 hour each ! A panel discussion with 45 minutes

Topics of Interest

Topics include but are not limited to deep learning based network architecture design, large scale deep learning training framework, high-performance online inference engine or toolkits that help breaking the black box of deep learning models, such as

  • Large Scale User Response Prediction Modeling
  • Representation Learning for High-dimensional Sparse Data
  • Embedding techniques, manifold learning and dictionary learning
  • User Behaviour Understanding
  • Large Scale Recommendation and Retrieval System
  • Model compression for industrial application
  • Scalable, Distributed and Parallel Training System for Deep Learning
  • High throughput and low latency real time Serving System
  • Applications of transfer learning, meta learning for sparse data
  • Auto Machine Learning, Auto feature selection
  • Explainable deep learning for high dimensional data
  • Data augmentation, Anomaly Detection for High-dimensional Sparse data
  • Generative Adversarial Network for sparse data
  • Other challenges encountered in real-world applications

Call for papers

Official call for papers page is on https://easychair.org/cfp/dlp-kdd2020

Submissions are invited on describing innovative research on real-world data systems and applications, industrial experiences and identification of challenges that deploy research ideas in practical applications. Work-in-progress papers are also encouraged.

Full-length papers (up to 9 pages) or extended abstracts (2-4 pages) are welcome. Submissions must be in PDF format and formatted according to the new Standard ACM Conference Proceedings Template.

Reviews are not double-blind, and author names and affiliations should be listed. Please use the KDD official guidelines to format your paper.

All submissions can be made through EasyChair using the following link: https://easychair.org/conferences/?conf=dlpkdd2020

For all the accepted paper, we provide the option that it could be archived in ACM Digtal Library. You may see the publication of last year at https://dl.acm.org/doi/proceedings/10.1145/3326937

If you have any questions about submissions or our workshop, please contact dlpkddworkshop@gmail.com

Workshop Chairs


Xiaoqiang Zhu
Tech Lead of advertising group
Alibaba

Kuang-chih Lee
Tech Lead of business intelligence group, AliExpress

Guorui Zhou
Algorithm expert of advertising group
Alibaba

Biye Jiang
Algorithm expert of advertising group
Alibaba

Liang Xiong
Facebook AI Applied Research
Junlin Zhang
Tech Lead
Artificial Intelligence Laboratory, Sina Weibo

Zhe Wang
Tech Lead
Recommendation group, Roku

Zheng Wen
Research Scientist
DeepMind
Haishan Liu
Director of Engineering
Tencent

Kan Ren
Microsoft Research

Qingyao Ai
Assistant Professor
University of Utah

Shandian Zhe
Assistant Professor
University of Utah

Weinan Zhang
Assistant Professor
Shanghai Jiao Tong University

Program Committee

Coming soon.