Tutorial 1


Causally Regularized Machine Learning


Peng Cui, Kun Kuang, Bo Li  


Owing to the popularity of Big Data, abundant data are accumulated in various domains such as health-care and advertising. At the same time, many machine learning methods are proposed to exploit these data for prediction, aiming to estimate the future outcome in the application of interest. These methods have been proved to be successful in prediction-oriented applications. However, the lack of interpretability of most predictive algorithms makes them less attractive in many settings, especially those requiring decision making. How to improve the interpretability of learning algorithms is of paramount importance for both academic research and real applications.

Causal inference, which refers to the process of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect, is a powerful statistical modeling tool for explanatory analysis. In this tutorial, we focus on causally regularized machine learning, where we aim to explore causal knowledge from observational data to improve the explainability and stability of machine learning algorithms. First, we will give some examples on how machine learning algorithms today focus on correlation analysis and prediction, and why those methods are not insufficient for decision making questions like What if. Then, we will give introduction to causal inference and introduce some recent data-driven approaches to explore causal knowledge or make causal inference from observational data, especially in high dimensional setting. Aiming to bridge the gap between causal inference and machine learning, we will introduce some recently causally regularized machine learning algorithms for improving the stability and interpretability of prediction. Finally, we will discussing future directions of the landscape of open research and challenges in machine learning with causal inference.

The goal of this tutorial is to bring machine learning practitioners closer to the vast field of causal inference as practiced by statisticians, epidemiologists and economists. We want to bridge the gap between causal inference and machine learning, and hope the machine learning methods in the future will be more powerful and interpretable under the help of causal inference.


Peng Cui is an Associate Professor in Tsinghua University. He got his PhD degree from Tsinghua University in 2010. His research interests include network representation learning, social dynamics modeling and human behavioral modeling. He has published more than 60 papers in prestigious conferences and journals in data mining and multimedia. His recent research won the ICDM 2015 Best Student Paper Award, SIGKDD 2014 Best Paper Finalist, IEEE ICME 2014 Best Paper Award, ACM MM12 Grand Challenge Multimodal Award, and MMM13 Best Paper Award. He is the Area Chair of ICDM 2016, ACM MM 2014-2015, IEEE ICME 2014-2015, ICASSP 2013, Associate Editor of IEEE TKDE, ACM TOMM, Elsevier Journal on Neurocomputing. He was the recipient of ACM China Rising Star Award in 2015.  

Kun Kuang received the B.E. degree from the Department of Computer Science and technology of Beijing Institute of Technology in 2014. He is a fifthyear Ph.D. candidate in the Department of Computer Science and Technology of Tsinghua University. His main research interests including data mining, high dimensional inference and data driven causal model. He has published several papers on data-driven causal inference and high dimensional inference in top data mining and machine learning conferences/journals of the relevant field such as SIGKDD, AAAI, and ICDM etc.  

Bo Li received a Ph.D degree in Statistics from the University of California, Berkeley, and a bachelor’s degree in Mathematics from Peking University. He is an Associate Professor at the School of Economics and Management, Tsinghua University. His research interests are statistical methods for high-dimensional data, statistical causal inference and data-driven decision making. He has published widely in academic journals across a range of fields including statistics, management science and economics.



Tutorial 2  


IoT Big Data Stream Mining  


Joao Gama, Albert Bifet , Latifur Khan  


The challenge of deriving insights from the Internet of Things (IoT) has been recognized as one of the most exciting and key opportunities for both academia and industry. Advanced analysis of big data streams from sensors and devices is bound to become a key area of data mining research as the number of applications requiring such processing increases. Dealing with the evolution over time of such data streams, i.e., with concepts that drift or change completely, is one of the core issues in IoT stream mining. This tutorial is a gentle introduction to mining IoT big data streams. The first part introduces data stream learners for classification, regression, clustering, and frequent pattern mining. The second part deals with scalability issues inherent in IoT applications, and discusses how to mine data streams on distributed engines such as Spark, Flink, Storm, and Samza.  


Albert Bifet is Full Professor at Telecom Paris Tech and Honorary Research Associate at the WEKA Machine Learning Group at University of Waikato. Previously he worked at Huawei Noah’s Ark Lab in Hong Kong, Yahoo Labs in Barcelona, University of Waikato and UPC BarcelonaTech. He is the author of a book on Adaptive Stream Mining and Pattern Learning and Mining from Evolving Data Streams. He is one of the leaders of MOA and Apache SAMOA software environments for implementing algorithms and running experiments for online learning from evolving data streams. He is serving as Co-Chair of the Industrial track of IEEE MDM 2016, ECML PKDD 2015, and as Co-Chair of BigMine (2015, 2014, 2013, 2012), and ACM SAC Data Streams Track (2016, 2015, 2014, 2013, 2012).  

Joao Gama received, in 2000, his Ph.D. degree in Computer Science from the Faculty of Sciences of the University of Porto, Portugal. He joined the Faculty of Economy where he holds the position of Associate Professor. He is also a senior researcher and vice-director of LIAAD, a group belonging to INESC TEC. He has worked in several National and European projects on Incremental and Adaptive learning systems, Ubiquitous Knowledge Discovery, Learning from Massive, and Structured Data, etc. He served as Co-Program chair of ECML’2005, DS’2009, ADMA’2009, IDA’ 2011, and ECM-PKDD’2015. He served as track chair on Data Streams with ACM SAC from 2007 till 2016. He organized a series of Workshops on Knowledge Discovery from Data Streams with ECMLPKDD conferences and Knowledge Discovery from Sensor Data with ACM SIGKDD. He is author of several books in Data Mining (in Portuguese) and authored a monograph on Knowledge Discovery from Data Streams. He authored more than 250 peer-reviewed papers in areas related to machine learning, data mining, and data streams. He is a member of the editorial board of international journals ML, DMKD, TKDE, IDA, NGC, and KAIS.  

Latifur Khan is a full Professor (tenured) in the Computer Science department at the University of Texas at Dallas where he has been teaching and conducting research since September 2000. He received his Ph.D. and M.S. degrees in Computer Science from the University of Southern California in August of 2000, and December of 1996 respectively. He has received prestigious awards including the IEEE Technical Achievement Award for Intelligence and Security Informatics. Dr. Khan is an ACM Distinguished Scientist and a Senior Member of IEEE. He has chaired several conferences and serves (or has served) as associate editor on multiple editorial boards including IEEE Transactions on Knowledge and Data Engineering (TKDE) journal. He has conducted tutorial sessions in prominent conferences such as ACM WWW 2005, MIS2005, DASFAA 2007, and WI 2008 ( ”Matching Words and Pictures Problems, Applications, and Progress” ) and PAKDD 2011 ( ”Data Stream Mining Challenges and Techniques”).



Tutorial 3



Knowledge Graph Embedding and Applications  


Guandong Xu, Shaowu Liu, Zili Zhou  


Knowledge Graph (KG), a large-scale semantic web including entities and relations between entities, formulizes the real-world facts into graph structure storage. KG Embedding is to extract the structural information of KG into a continuous vector space, so as to manipulate the KG with latent semantic information of KG components. The KG Embedding techniques can be used in the completion task of current KG which is collected from multiple World Wide Web resources but still far from complete. KG Embedding techniques can also support the decision in several Out-of-KG applications such as Relation Extraction (RE), Question Answering (QA) System and Recommender system (RS).  

In this tutorial, we will systematically address the concepts and techniques of KG Construction, KG Embedding, and KG Application. Specifically, the resources and construction methods of KG will be introduced. Then, we will introduce the state-of-the-art KG Embedding approaches, e.g., Translational Distance Models, Semantic Matching Models, and Relation Path-based Models. After that, we will focus on recent advances in KG completion with Embedding. Then we discuss the start-of-the-art algorithms of applying KG in other related systems, such as applications in RE, QA, and RS. Finally, we conclude and present open research for the future.  

This tutorial targets at the audiences who are conducting researches or pursuing research degrees in related areas, and who are working in the engineering domains of knowledge discovery, knowledge representation, knowledge inference, knowledge application and so on. The whole tutorial expects to last for three hours.  


Guandong Xu received the Ph.D. degree in Computer Science from Victoria University, Australia. He is currently an Associate Professor (Reader) in School of Software and the Advanced Analytics Institute at University of Technology Sydney. He has authored three monographs with the Springer and the CRC Press, and 100+ journal and conference papers. His current research interests include data science and data analytics, Web data mining, behaviour analytics, recommender systems, predictive analytics, social network analysis. His research has gained grant funding from Australian and Chinese governments, e.g., ARC and NSFC grants, and projects funded by industries. In last decade, he has had over 100+ publications including TOIS, TNNLS, TIFS, TSC, Inf Sci, IEEE-IS, IJCAI, AAAI, WWW, ICDE, ICDM, and CIKM. He is the Assistant EiC of WWW Journal plus serving in the Editorial Board or as Guest Editor for several international journals. He received Australian BigInsight Data Analytics Award in December 2016 due to his significant impact on Best Customer Insights.  

Shaowu Liu received his doctorate from Deakin University in the field of machine learning. He is currently a postdoctoral research fellow at University of Technology Sydney. Since 2012, he has published 25+ papers in the arena of data mining and machine learning, including Machine Learning Journal (MLJ), Future Generation Computer Systems (FGCS), IEEE Transactions on SMC(C), Enterprise Information Systems (EIS), and AAAI. For the community, he has served as co-chair of ES 2016, IIP 2016, KSEM 2017, and KSEM 2019.  

Zili Zhou is currently a PhD candidate of School of Software, Faculty of Engineering & IT, University of Technology Sydney. His research interests include knowledge graph representation learning, knowledge inference and knowledge graph application. Since 2017, he has published several papers in area of data mining, machine learning and knowledge graph, including international joint conference on neural networks (IJCNN), The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) and AAAI.



Tutorial 4



Statistical Machine Learning of Large, Sparse, and Multi-source Data  


Trong Dinh Thac Do, Longbing Cao  


With the explosion of data on the internet, social networks, finance, and e-commerce websites, modelling large and sparse datasets is highly in demand yet challenging. However, traditional methods face problems in handling these real-life datasets because of the intensive mathematical computation required. Although several statistical methods are proposed to handle a large amount of data, they are still inefficient for sparse data. It is because they perform computation on the whole datasets. However, in many real-life applications, a large amount of data are missing (i.e., sparse data). For example, in recommender systems, e.g., the Netflix data has 98.8% of the matrix entries missing.

In this tutorial, we summarize various statistical methods that are effective and efficient in handling large and sparse datasets.

On the other hand, combining observable data; e.g., users’ ratings on items in recommender systems, user friendship or item relations, and user/item metadata; helps to deal with cold-start problems where we have no or limited preliminary knowledge about one specific element. Accordingly, we focus on introducing our series of design for tackling these challenges on large, sparse and multi-source data. These designs perform the computation only on non-missing data and combine with efficient Bayesian inference methods.

We will show that in-depth knowledge of statistical methods for large, sparse and multi-source data creates new opportunities, directions, and means for, learning and analysis of complex and practical machine learning problems.


Trong Dinh Thac Do is currently a Ph.D. candidate in the Advanced Analytics Institute (AAI) at the University of Technology Sydney (UTS). Before joining AAI-UTS, he received his Master of Philosophy degree in Information Technology in a joint program from the Grenoble Institute of Technology (INPG) and Joseph Fourier University (UJF), France. His research interests include machine learning, statistical models and Bayesian nonparametrics. He has been working in applied machine learning applications since he was a CTO and software engineer. He has published several papers in refereed conferences and journals, such as NIPS, AAAI, IJCAI, ICDM, and International Journal of Knowledge and Information Systems (KAIS). He has served the community as program committee member of AAAI 2019, ICDM 2018, ICDM 2017, PAKDD 2019, PAKDD 2018, and reviewer of IEEE Intelligent Systems, International Journal of Knowledge and Information Systems (KAIS), and International Journal of Data Science and Analytics (JDSA).  

Professor Longbing Cao holds a Ph.D. in Pattern Recognition and Intelligent Systems in Chinese Academy of Sciences and another Ph.D. in Computing Science at UTS. He has published some 300 publications, four monographs, and four edited books in recent 15 years. He has been working on data science and analytics research, education, development, and enterprise applications since he was a CTO and then joined UTS. Motivated by real-world significant and common challenges, he has been leading the team to develop theories, tools and applications for new areas including non-IID learning, actionable knowledge discovery, behaviour informatics, and complex intelligent systems, in addition to issues generally concerned in artificial intelligence, knowledge discovery, machine learning, and their enterprise applications. In data science and analytics, he initiated the Data Science and Knowledge Discovery lab at UTS in 2007, the Advanced Analytics Institute in 2011, the degrees Master of Analytics (Research) and PhD in Analytics in 2011 which are recognized as the world first degrees in data science, the IEEE Task Force on Data Science and Advanced Analytics (DSAA) and IEEE Task Force on Behavior, Economic and Soci-cultural Computing in 2013, the IEEE Conference on Data Science and Advanced Analytics (DSAA), the ACM SIGKDD Australia and New Zealand Chapter in 2014, and the International Journal of Data Science and Analytics with Springer in 2015. He served as program and general chairs of conferences such as KDD2015. In enterprise data science innovation, his team has successfully delivered many large projects for government and business organizations in over 10 domains including finance/capital markets, banking, health and car insurance, health, telco, recommendation, online business, education, and the public sector including ATO, DFS, DHS, DIBP and IP Australia, resulting in billions of dollar savings and mentions in government, industry, media and OECD reports. In 2013, AAI was the only organization specially mentioned in the Governments first big data paper: Big Data Strategy Issues Paper. He has delivered invited and keynote speeches to over 20 conferences, guest lectures, and seminars to many universities, and tutorials to conferences including AAAI, IJCAI, and KDD.


Tutorial 5


Building and evaluating a production-ready recommendation system  


Le Zhang, Graham Williams, Tao Wu, Miguel Gonzalez-Fierro, Nikhil Joglekar  


Recent decades witnessed grand proliferation of recommendation systems. The technology has brought tremendous profits to business in the verticals of retail, entertainment, etc. Research in the field has been heated from the earlier algorithm such as similarity based collaborative filtering, to the latest deep neural network based method, recommendation technologies have evolved dramatically, which, to some extent, makes it challenging to fresh practitioners to select and then customize the optimal algorithms for a specific business scenario. In addition, it is often observed that auxiliary operations such as data preprocessing, model evaluating, etc., which often play an equally significant role in the whole lifecycle of developing a recommendation system, should be but not attached with enough importance by developers.  

Based on the extensive experience in productizing real-world recommendation systems, in this tutorial, the authors review and lecture key tasks in building recommendation systems, with best-practice examples to democratize the technology to every organization. An open source Github repository, where the aforementioned topics are shaped into Jupyter notebooks and utility function codebase, will be used for hands-on practice. Several recommendation algorithms will be walked through to provide an in-depth understanding of the techniques. In general, the best-practice examples shared in the repository help developers / scientists / researchers to both quickly build production-ready recommendation system and prototype novel ideas with the provided utility functions.  


Le Zhang is Data Scientist with Microsoft Cloud and Artificial Intelligence. He has extensive experience on applying the cutting-edge machine learning and artificial intelligence technology to accelerate digital transformation for enterprises and start-ups. He has helped numerous corporations to develop and build enterprise-grade scalable advanced data analytical system, for scenarios of recommendation system, smart manufacturing, predictive maintenance, financial services, e-commerce, human resource analytics, etc. He specializes in artificial intelligence and machine learning.  

Graham Williams is Microsoft's Director of Data Science for Asia based in Singapore. He joined Microsoft after over 30 years of research, development, practice and teaching in Artificial Intelligence, Machine Learning, Data Mining, Analytics and Data Science. He has worked extensively in the open source ecosystem as a regular contributor to numerous open source software projects including Linux and R. Graham has authored a number of books, papers, internet resources and software packages for data scientists.  

Tao Wu is a Principal Data Scientist manager with Microsoft Cloud & AI team – he leads a team with special on recommendation system technology. Tao has been part of a team that has won contests in speech recognition in China three years in a row.  

Miguel Gonzalez-Fierro is a Senior Data Scientist with Microsoft Cloud & AI team, where has worked with several Microsoft enterprise customers on deep learning and recommendation projects over the last two years. Miguel is an active deep learning blogger and has founded two startups.  

Nikhil Joglekar is a Program Manager with Microsoft Cloud & AI team. Nikhil focuses on accelerating customer journeys and investments in AI and machine learning projects, with a focus on recommender systems.


Tutorial 6



Blockchain Data Analytics  


Cuneyt Gurcan Akcora, Yulia R. Gel, Murat Kantarcioglu  


Over the last couple of years, Bitcoin cryptocurrency and the Blockchain technology that forms the basis of Bitcoin have witnessed an unprecedented attention.  

Designed to facilitate a secure distributed platform without central regulation, Blockchain is heralded as a novel paradigm that will be as powerful as Big Data, Cloud Computing, and Machine Learning.  

The Blockchain technology garners an ever-increasing interest of researchers in various domains that benefit from scalable cooperation among trust-less parties. Some of these fields, such as graph analytics, have started analyzing Blockchain by using existing tools and algorithms, but have also offered novel approaches that are specifically tailored for Blockchain data. As Blockchain data analytics further proliferates, a need to glean successful approaches and to disseminate them among a diverse body of data scientists became a critical task. As an inter-disciplinary team of researchers, our aim is to fill this vital role.  

We offer a holistic view on Blockchain Data Analytics. Starting with the core components of Blockchain, we will detail the state of art in Blockchain data analytics for graph, security and finance domains. Beyond the cryptocurrency aspects of Blockchain, we will outline the frontier research approaches for data analyses from Blockchain platforms, such as Ethereum, Waves and Omni.  

We will share tutorial notes, collected meta-information and further reading pointers on our tutorial website.  


Cuneyt Gurcan Akcora is a Postdoctoral Fellow in the Departments of Statistics and Computer Science at the University of Texas at Dallas. He received his Ph.D. from University of Insubria, Italy and his M.S. from State University of New York at Buffalo, USA. His primary research interests are Data Science on complex networks and large scale graph analysis, with applications in social, biological, IoT and Blockchain networks. He is a Fulbright Scholarship recipient, and his research works have been published in leading conferences and journals including VLDB, ICDM and ICDE.  

Yulia R. Gel is Professor in the Department of Mathematical Science at the University of Texas at Dallas. Her research interests include statistical foundation of Data Science, inference for random graphs and complex networks, time series analysis, and predictive analytics. She holds a Ph.D in Mathematics, followed by a postdoctoral position in Statistics at the University of Washington. Prior to joining UT Dallas, she was a tenured faculty member at the University of Waterloo, Canada. She also held visiting positions at Johns Hopkins University, University of California, Berkeley, and the Isaac Newton Institute for Mathematical Sciences, Cambridge University, UK. She served as a Vice President of the International Society on Business and Industrial Statistics (ISBIS), and is a Fellow of the American Statistical Association.  

Murat Kantarcioglu is a Professor in the Computer Science Department and Director of the UTD Data Security and Privacy Lab at the University of Texas at Dallas and a visiting scholar at Harvard University Data Privacy Lab. He is a recipient of NSF CAREER award, and Purdue CERIAS Diamond Award for Academic excellence. His research focuses on creating technologies that can efficiently extract useful information from any data without sacrificing privacy or security. Over the years, his research has been supported by grants from NSF, AFOSR, ONR, NSA, and NIH. In addition, he has published over 160 peer reviewed papers related to data security, privacy and privacy-preserving data mining. Some of his research work has been covered by the media outlets, such as Boston Globe, ABC News, and has received three best paper awards.

Important Dates

* Paper Submission Due

October 10, 2018
October 17, 2018

* Notification to Authors
December 15, 2018

* Camera-ready Due
January 15, 2019

* Conference Date
April 14-17, 2019

All deadlines are 11:59pm Pacific Standard Time (PST)