AutoML: From Methodology to Application

Tutorial Website https://joneswong.github.io/CIKM21AutoMLTutorial/

Tutorial Schedule

  • times in UTC
  • 5:00AM–5:10AM Welcome from Organizers (by Yaliang Li)
  • 5:10AM–5:40AM Hyperparameter Optimization (HPO) (by Yaliang Li)
  • 5:40AM–6:15AM Neural Architecture Search (NAS) (by Zhen Wang)
  • 6:15AM–6:30AM Meta-learning (by Zhen Wang)
  • 6:30AM–7:00AM Auto Feature Generation (by Yuexiang Xie)
  • 7:00AM–7:25AM End-to-End AutoML (by Ce Zhang)
  • 7:25AM–7:50AM ML-Guided Database: Learned Index (by Bolin Ding)
  • 7:50AM–9:50AM ML-Guided Database: Cardinality Estimation (by Rong Zhu and Kai Zeng)
  • 9:50AM–9:55AM AutoML Tools (by Zhen Wang)
  • 9:55AM–10:00AM Closing Remarks (by Yaliang Li)

Tutorial Description

Machine Learning methods have been adopted for a wide range of real-world applications, ranging from social networks, online image/video-sharing platforms, and e-commerce to education, healthcare, etc. However, in practice, a large amount of effort is required to tune several components of machine learning methods, including data representation, hyperparameter, and model architecture, in order to achieve a good performance. To alleviate the required tunning efforts, Automated Machine Learning (AutoML), which can automate the process of applying machine learning methods, has been studied in both academy and industry recently. In this tutorial, we will introduce the main research topics of AutoML, including Hyperparameter Optimization, Neural Architecture Search, and Meta-Learning. Two emerging topics of AutoML, Automatic Feature Generation and Machine Learning Guided Database, will also be discussed since they are important components for real-world applications. For each topic, we will motivate it with application examples from industry, illustrate the state-of-the-art methodologies, and discuss some future research directions based on our experience from industry and the trends in academy.

Tutorial Organisers

  • Head shot of Yaliang Li
    Yaliang Li
    Alibaba Group
    Dr. Yaliang Li is a research scientist in the Data Analytics and Intelligence Lab (DAIL) at Alibaba Group. He received the Ph.D. degree from the Department of Computer Science and Engineering at SUNY Buffalo in 2017. Prior to joining Alibaba, he worked as a research scientist at Baidu Research, and a senior researcher at Tencent Medical Lab. He has published 60 papers at top-tier conferences and journals, including KDD, ACL, VLDB, SIGMOD, NeurIPS, SIGIR, TKDE, etc. He served as co-chair for IJCAI-TUSION (2019, 2020) workshops, Area Chair for NeurIPS'21, AAAI'22, Senior PC member for AAAI'20, PC member for top-tier conferences and journals such as KDD, ACL, NeurIPS and TKDE. He gave tutorials at KDD'20, AAAI'20 and KDD'21.
  • Head shot of Zhen Wang
    Zhen Wang
    Alibaba Group
    Dr. Zhen Wang is a research scientist in the Data Analytics and Intelligence Lab (DAIL) at Alibaba Group. He received the Ph.D. degree after a joint Ph.D. program of Sun Yat-sen University and Microsoft Research Asian. He transferred many AutoML algorithms to the AI utilities that serve the customers of Alibaba Cloud.
  • Head shot of Yuexiang Xie
    Yuexiang Xie
    Alibaba Group
    Yuexiang Xie is a research scientist in the Data Analytics and Intelligence Lab (DAIL) at Alibaba Group. He received the Master's Degree in Computer Application Technology at Peking University in 2020. His research focuses on Automated Machine Learning and Natural Language Processing, including Automated Feature Generation, Text Generation, and Question Answering. He has published several papers in top conferences and journals in related areas, including EMNLP, KDD, AAAI, TOIS, etc.
  • Head shot of Bolin Ding
    Bolin Ding
    Alibaba Group
    Dr. Bolin Ding is a research scientist in the Data Analytics and Intelligence Lab (DAIL) at Alibaba Group. He completed his Ph.D. in Computer Science at University of Illinois at Urbana-Champaign, M.Phil. in Systems Engineering and Engineering Management at The Chinese University of Hong Kong, and B.S. in Math and Applied Mathematics at Renmin University of China. Prior to joining Alibaba, he worked as a researcher in Microsoft Research. His research focuses on the management and analytics of large-scale data, including real-time approximate query algorithms and systems, data privacy protection, query processing and optimization algorithms, and algorithms and applications of data mining and machine learning. He holds more than 10 US patents. He received the 2017 Technical Excellence Award from Microsoft Privacy for his contributions on the research and deployment of data privacy techniques. He has published more than 60 papers in top conferences and journals in related areas, including NeurIPS, ICML, SIGMOD, VLDB, ICDE, KDD, CHI, and AAAI.
  • Head shot of Kai Zeng
    Kai Zeng
    Alibaba Group
    Dr. Kai Zeng is a research scientist in the Data Analytics and Intelligence Lab (DAIL) at Alibaba Group. Dr. Zeng received his Ph.D. in Computer Science from the University of California Los Angeles. Before joining Alibaba, he was a Senior Scientist at Microsoft Cloud and Information Service Lab, and a postdoc researcher at AMPLab, Univeristy of California Berkeley before that. His research interest focuses on large-scale distributed systems and database systems. He has published papers in top database journals and conferences (including SIGMOD, VLDB, ICDE, TODS, and so on). He has received the SIGMOD Best Paper Award in 2012 and the SIGMOD Best Demonstration Award in 2014, and was nominated for the SGIMOD Best Demonstration Award in 2010.
  • Head shot of Ce Zhang
    Ce Zhang
    ETH Zürich
    Dr. Ce Zhang is an Assistant Professor in Computer Science at ETH Z{\"u}ric. His current research focuses on building data systems to support machine learning and help facilitate other sciences. Before joining ETH, Ce was advised by Christopher Ré. He finished his Ph.D. round-tripping between the University of Wisconsin-Madison and Stanford University, and spent another year as a postdoctoral researcher at Stanford. He contributed to the research efforts that won the SIGMOD Best Paper Award and SIGMOD Research Highlight Award, and was featured in special issues including the Science magazine, the Communications of the ACM, “Best of VLDB”, and the Nature magazine. His work has also been reported by the Atlantic, WIRED, Quanta Magazine, the Verge, etc.
  • Head shot of Rong Zhu
    Rong Zhu
    Alibaba Group
    Dr. Rong Zhu is a research scientist in the Data Analtics and Intelligence Lab (DAIL) at Alibaba Group. He obtained the Ph.D. and B.S. degree from Harbin Institute of Technology in 2019 and 2013, respectively. His research focuses on data management and analytics, including graph data mining, graph processing system and intelligent databases. He is nominated the China Computer Federation (CCF) Outstanding Doctoral Dissertation Award in 2020. He has published more than 20 papers in top-tier conferences and journals, including VLDB Journal, VLDB, TKDE, ICDE, ICLR and etc, and served as PC members for KDD, CIKM, SDM and etc.

Tutorial Abstract

Recently, there has been growing attention on fairness considerations in machine learning. As one of the most pervasive applications of machine learning, recommender systems are gaining increasing and critical impacts on human and society since a growing number of users use them for information seeking and decision making. Therefore, it is crucial to address the potential unfairness problems in recommendation, which may hurt users' or providers' satisfaction in recommender systems as well as the interests of the platforms. The tutorial focuses on the foundations and algorithms for fairness in recommendation. It also presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking. The tutorial will introduce the taxonomies of current fairness definitions and evaluation metrics for fairness concerns. We will introduce previous works about fairness in recommendation and also put forward future fairness research directions. The tutorial aims at introducing and communicating fairness in recommendation methods to the community, as well as gathering researchers and practitioners interested in this research direction for discussions, idea communications, and research promotions.