Tutorials & Workshops



  • MML 2019: 12th International Workshop on Machine Learning and Music 🔗

    Machine learning and artificial intelligence have permeated nearly every area of music informatics, driven by a profusion of recordings available in digital audio formats, steady improvements to the accessibility and quality of symbolic corpora, availability of powerful algorithms, and theoretical advances in machine learning and data mining. As the complexity of the problems investigated by researchers on machine learning and music increases, there is a need to develop new algorithms and methods. The 12th International Workshop on Machine Learning and Music (MML 2019) aims to promote fruitful multidisciplinary collaboration among researchers who are using machine learning techniques in musical applications, by providing the opportunity to discuss ongoing work in the area. MML 2019 welcomes papers in all applications on music and machine learning.

    • Rafael Ramirez, Universitat Pompeu Fabra, Spain
    • Darrell Conklin, University of the Basque Country, Spain
    • José Manuel Iñesta, University of Alicante, Spain
    Website: https://musml2019.weebly.com/ 🔗
  • Workshop on Multiple-aspect analysis of semantic trajectories (MASTER2019) 🔗

    An ever-increasing number of diverse, real-life applications, ranging from mobile to social media apps and surveillance systems, produce massive amounts of spatio-temporal data representing trajectories of moving objects. The fusion of those trajectories, commonly represented by timestamped location sequence data (e.g. check-ins and GPS traces), with generally available and semantic-rich data resources can result in an enriched set of more comprehensive and semantically significant objects. The analysis of these sets, referred to as "semantic trajectories", can unveil solutions to traditional problems and unlock the challenges for the advent of novel applications and application domains, such as transportation, security, health, environment and even policy modeling. The MASTER 2019 workshop anticipates to solicit high-quality, scientifically sound and innovative contributions on the topic with the intention to promote and follow the developments in the domain.

    Organizers: Website: http://www.master-project-h2020.eu/workshop-master-2019/ 🔗
  • MIDAS - The 4th Workshop on MIning DAta for financial applicationS 🔗

    Like the famous King Midas, popularly remembered in Greek mythology for his ability to turn everything he touched with his hand into gold, we believe that the wealth of data generated by modern technologies, with widespread presence of computers, users and media connected by Internet, is a goldmine for tackling a variety of problems in the financial domain. The MIDAS workshop is aimed at discussing challenges, potentialities, and applications of leveraging data-mining tasks to tackle problems in the financial domain. The workshop provides a premier forum for sharing findings, knowledge, insights, experience and lessons learned from mining data generated in various application domains.

    • Valerio Bitetta, Unicredit, R&D Dept., valerio.bitetta[at]unicredit.eu
    • Ilaria Bordino, Unicredit, R&D Dept., ilaria.bordino[at]unicredit.eu
    • Andrea Ferretti, Unicredit, R&D Dept., andrea.ferretti2[at]unicredit.eu
    • Francesco Gullo, Unicredit, R&D Dept., francesco.gullo[at]unicredit.eu
    • Stefano Pascolutti, Unicredit, R&D Dept., stefano.pascolutti[at]unicredit.eu
    • Giovanni Ponti, ENEA, Portici Research Center, giovanni.ponti[at]enea.it
    Website: http://midas.portici.enea.it 🔗
  • Second International Workshop on Knowledge Discovery and User Modeling for Smart Cities 🔗

    User modelling and personalization are commonly used in multiple tasks, in which users are characterized only based on explicit information about their knowledge, behaviour, social relations or preferences, aiming at adapting generic systems to the particularities of each user. The ubiquitousness of social networking sites, and mobile and smart-devices offer new information sources, opportunities and challenges for changing the personalization paradigm. The analysis of these new data source offer new research opportunities across a wide variety of disciplines, including media and communication studies, linguistics, sociology, health, psychology, information and computer sciences, or education. This has important implications in the context of inclusive eGovernment and Smart Cities, which could leverage on the user’s models to design and tailor services according to the characteristics and needs of each particular citizen. In this context, this workshop targets academy and industrial practitioners leveraging on diverse data mining and machine learning techniques, including content aggregation, content analysis, predictive modeling, deep learning and user embedding for modeling user behavior and analyzing urban data.

    • Rabeah Alzaidy, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
    • Marcelo G. Armentano, ISISTAN, CONICET-UNICEN. Buenos Aires, Argentina
    • Antonela Tommasel, ISISTAN, CONICET-UNICEN. Buenos Aires, Argentina
    • Ludovico Boratto, Eurecat, Barcelona, Spain
    • Clyde L. Giles, College of Information Sciences and Technology, Pennsylvania State University
    Website: https://umcit-2019.isistan.unicen.edu.ar/ 🔗
  • New Frontiers in Mining Complex Patterns 🔗

    Modern automatic systems are able to collect huge volumes of data often with a complex structure. The massive and complex data pose new challenges for current research in Knowledge Discovery and Data Mining. They require new methods for storing, managing and analyzing them by taking into account various complexity aspects: Complex structures (e.g. multi-relational, time series and sequences, networks, and trees) as input/output of the data mining process; Massive amounts of high dimensional data collections flooding as high-speed streams and requiring (near) real time processing and model adaptation to concept drifts; New application scenarios involving security issues, interaction with other entities and real-time response to events triggered by sensors. The purpose of the workshop is to bring together researchers and practitioners of data mining and machine learning interested in analysis of complex and massive data sources such as blogs, event or log data, medical data, spatio-temporal data, social networks, mobility data, sensor data and streams.

    • Michelangelo Ceci, University of Bari Aldo Moro, Bari, Italy
    • Corrado Loglisci, University of Bari Aldo Moro, Bari, Italy
    • Giuseppe Manco, ICAR-CNR, Rende, Italy
    • Elio Masciari, ICAR-CNR, Rende, Italy
    • Zbigniew W. Ras, University of North Carolina, Charlotte, USA & Institute of Computer Science, Warsaw University of Technology, Poland
    Website: http://www.di.uniba.it/~loglisci/NFMCP2019/index.html 🔗
  • New Trends in Representation Learning with Knowledge Graphs 🔗

    Knowledge Graphs are becoming the standard for storing, retrieving and querying structured data. In academia and in industry, they are increasingly used to provide background knowledge. Over the last years, several research contributions are made to show machine learning especially representation learning is successfully applied to knowledge graphs enabling inductive inference about facts with unknown truth values. In this workshop we intend to focus on the most exciting new developments in Knowledge Graph learning, bridging the gap to recent developments in different fields. Also, we want to bring together researchers from different disciplines but united by their adoption of earlier mentioned techniques from machine learning.

    • Volker Tresp (Ludwig-Maximilians University and Siemens, Germany)
    • Jens Lehmann (Bonn University and Fraunhofer IAIS, Germany)
    • Aditya Mogadala (Saarland University, Germany)
    • Achim Rettinger (Trier University, Germany)
    • Afshin Sadeghi (Fraunhofer IAIS, Germany)
    • Mehdi Ali (Bonn University and Fraunhofer IAIS, Germany)
    Website: https://sites.google.com/view/kgrlfr-workshop/home 🔗
  • HummeL: Humanities meets Learning - Challenges for Computational Literary Studies 🔗

    Digital Humanities have since their beginning in the 1940s adapted methods from computer science. In recent years the time span between invention in computer science and adaption in Digital Humanities has been reduced dramatically and today applications of ‘traditional’ machine learning and deep learning can be found in many DH papers. Nevertheless, direct exchange between researchers in machine learning and in the digital humanities is still rare. In this workshop, we want to bring together both communities and encourage cooperation between researchers from machine learning, natural language processing and digital humanities. This workshop puts a special focus on the development and application of methods in Natural Language Processing in combination with Machine Learning to literary texts.

    • Andreas Hotho, University of Wuerzburg
    • Fotis Jannidis, University of Wuerzburg
    • Leonard Konle, University of Wuerzburg
    • Albin Zehe, University of Wuerzburg
    Website: https://hummel.dmir.org/ 🔗
  • Second International Workshop on Energy Efficient Scalable Data Mining and Machine Learning. Green Data Mining 🔗

    This workshop aims to bring together people from different areas and backgrounds in data mining and machine learning that have a common interest in scalability, edge computing, and energy efficiency. These fields include, but are not limited to: deep learning, Internet of Things (IoT), scalable machine learning, systems for machine learning, information retrieval systems, and stream mining. The goal is to provide a venue where researchers with heterogeneous backgrounds can debate and discuss challenges related to building energy efficient machine learning algorithms, systems, and hardware platforms. We accept original work, already completed, or in progress. Position papers are also welcomed.

    • Eva Garcia-Martin
    • Albert Bifet
    • Crefeda Faviola Rodrigues
    • Heitor Murilo Gomes
    Website: https://greendatamining.github.io 🔗
  • ECML/PKDD 2019 Workshop on Deep Continuous-Discrete Machine Learning (DeCoDeML 2019) 🔗

    Since the beginnings of machine learning – and indeed already hinted at in Alan Turing’s groundbreaking 1950 paper ”Computing machinery and intelligence” – two opposing approaches have been pursued: On the one hand, approaches that relate learning to knowledge and mostly use ”discrete” formalisms of formal logic. On the other hand, approaches which, mostly motivated by biological models, investigate learning in artificial neural networks and predominantly use ”continuous” methods from numerical optimization and statistics. The recent successes of deep learning can be attributed to the latter, the ”continuous” approach, and are currently opening up new opportunities for computers to ”perceive” the world and to act, with far reaching consequences for industry, science and society. The massive success in recognizing ”continuous” patterns is the catalyst for a new enthusiasm for artificial intelligence methods. However, today’s artificial neural networks are hardly suitable for learning and understanding ”discrete” logical structures, and this is one of the major hurdles to further progress. Accordingly, one of the biggest open problems is to clarify the connection between these two learning approaches (logical-discrete, neural-continuous). In particular, the role and benefits of prior knowledge need to be reassessed and clarified. The role of formal logic in ensuring sound reasoning must be related to perception through deep networks. Further, the question of how to use prior knowledge to make the results of deep learning more stable, and to explain and justify them, is to be discussed. The extraction of symbolic knowledge from networks is a topic that needs to be reexamined against the background of the successes of deep learning. Finally, it is an open question if and how the principles responsible for the success of deep learning methods can be transferred to symbolic learning. The First Workshop on Deep Continuous-Discrete Machine Learning (DeCoDeML) at ECML PKDD 2019 intends to attract papers on how recent deep learning methods can be connected to discrete structures and symbolic knowledge, addressing all of the above questions.

    Organizers: Website: https://sites.google.com/view/decodeml-workshop-2019 🔗
  • Decentralized Machine Learning at the Edge 🔗

    Many of today's parallel machine learning algorithms were developed for tightly coupled systems like computing clusters or clouds. However, the volumes of data generated from machine-to-machine interaction, by mobile phones or autonomous vehicles, surpass the amount of data that can be realistically centralized. Thus, traditional cloud computing approaches are rendered infeasible. To scale parallel machine learning to such volumes of data, computation needs to be pushed towards the edge, that is, towards the data generating devices. By learning models directly on the data sources---which often have computational power of their own, for example, mobile phones, smart sensors, and vehicles---network communication can be reduced by orders of magnitude. Moreover, it enables training a central model without centralizing privacy-sensitive data. This workshop is the second edition of the successful DMLE workshop at last year's ECMLPKDD. It aims to foster discussion, discovery, and dissemination of novel ideas and approaches for decentralized machine learning.

    • Michael Kamp
    • Yamuna Krishnamurthy
    • Daniel Paurat
    Website: https://dmle.iais.fraunhofer.de/ 🔗
  • Applications of Topological Data Analysis 🔗

    The emergent area of Topological data analysis (TDA) aims to uncover hidden structure in a wide variety of data sets, combining methods from algebraic topology and other tools of pure mathematics to study the shape of data. Though the pure mathematical foundation of TDA is a major research topic on its own, TDA has been applied to a wide variety of real world problems, among which image compression, cancer research, and shape or pattern recognition are only a few of the many examples. As TDA is generally not a well-known topic to the data mining and machine learning community, this workshop aims to address the flow of information between the different communities. By illustrating some of its recent and new applications, we will discuss the potential of TDA to active researchers within the fields of data science and machine learning. Furthermore, this workshop provides new and young TDA researchers a chance to present their work to a new community in an interesting and creative way, emphasizing the many possible applications of TDA in real-world data sets.

    • Robin Vandaele
    • Tijl De Bie
    • John Harer
    Website: https://sites.google.com/view/atda2019 🔗
  • GEM: Graph Embedding and Mining 🔗

    Graphs enable the specification of relational structure between entities, adding significant additional flexibility in comparison to data consisting of unrelated data points. The ability to model and discover knowledge from such network data is therefore fast gaining in importance. This workshop aims to bring together researchers and practitioners in graph mining, modeling, and embedding. We solicit papers that advance the state of the art for particular applications or introduce new algorithms for machine learning, embedding, or pattern mining on graphs.

    • Bo Kang, Ghent University
    • Remy Cazabet, Université de Lyon
    • Christine Largeron, Université Jean Monnet
    • Polo Chau, Georgia Tech
    • Jefrey Lijffijt, Ghent University
    • Tijl De Bie, Ghent University
    Website: https://gem-ecmlpkdd.github.io 🔗


  • Machine Learning for Cybersecurity (MLCS) 🔗

    The last decade has been a critical one regarding cybersecurity, with studies estimating the cost of cybercrime to be up to 0.8 percent of the global GDP. The capability to detect, analyze, and defend against threats in (near) real-time conditions is not possible without employing machine learning techniques and big data infrastructure. This gives rise to cyberthreat intelligence and analytic solutions, such as (informed) machine learning on big data and open-source intelligence, to perceive, reason, learn, and act against cyber adversary techniques and actions. Moreover, organisations’ security analysts have to manage and protect systems and deal with the privacy and security of all personal and institutional data under their control. The aim of this workshop is to provide researchers with a forum to exchange and discuss scientific contributions, open challenges and recent achievements in machine learning and their role in the development of secure systems.

    • Annalisa Appice
    • Battista Biggio
    • Donato Malerba
    • Fabio Roli
    • Ibéria Medeiros
    • Michael Kamp
    • Pedro Ferreira
    Website: http://mlcs.lasige.di.fc.ul.pt/ 🔗
  • BioASQ: Large-scale biomedical semantic indexing and question answering 🔗

    BioASQ is a series of workshops and challenges (shared tasks) that reward highly precise biomedical information access and machine learning systems. The aim of BioASQ is to push the research frontier towards systems that use the diverse and voluminous information available online to respond directly to the information needs of biomedical scientists.

    • George Paliouras (NCSR "Demokritos", Greece and University of Houston, USA)
    • Anastasia Krithara (NCSR "Demokritos", Greece)
    • Anastasios Nentidis (NCSR "Demokritos", Greece and Aristotle University of Thessaloniki, Greece)
    Website: http://www.bioasq.org/workshop 🔗
  • 6th Workshop on Sports Analytics: Machine Learning and Data Mining for Sports Analytics (MLSA) 🔗

    Sports Analytics has been a steadily growing and rapidly evolving area over the last decade that has attracted significant attention from professional clubs, researchers, and fans. This area has come to rely on techniques from machine learning and data mining. In terms of tasks, this field is very broad and it encompasses areas such as analyzing match strategy and tactics, valuing and rating players, designing training regimes, preventing injuries, predicting the outcomes of matches, and designing tournaments and schedules. The workshop solicits papers covering both predictive and descriptive Machine Learning, Data Mining, and related approaches to Sports Analytics settings. Adopting a broad definition of sports, the workshop is also open to submissions on electronic sports (i.e., e-sports) as well.

    • Jesse Davis, KU Leuven
    • Ulf Brefeld, Leuphana University
    • Jan Van Haaren, SciSports
    • Albrecht Zimmermann, University of Caen
    Website: https://dtai.cs.kuleuven.be/events/MLSA19/ 🔗
  • 4th workshop on Advanced Analytics and Learning on Temporal Data 🔗

    Temporal data are frequently encountered in a wide range of domains such as bio-informatics, medicine, finance and engineering, among many others. They are naturally present in applications covering language, motion and vision analysis, or more emerging ones as energy efficient building, smart cities, dynamic social media or sensor networks. Contrary to static data, temporal data are of complex nature, they are generally noisy, of high dimensionality, they may be non stationary (i.e. first order statistics vary with time) and irregular (involving several time granularities), they may have several invariant domain-dependent factors as time delay, translation, scale or tendency effects. The aim of this workshop is to bring together researchers and experts in machine learning, data mining, pattern analysis and statistics to share their challenging issues and advance researches on temporal data analysis. Analysis and learning from temporal data cover a wide scope of tasks including learning metrics, learning representations, unsupervised feature extraction, clustering and classification.

    • Anthony Bagnall - University of East Anglia, England
    • Alexis Bondu - Orange Labs, France
    • Pádraig Cunningham - University College Dublin, Ireland
    • Thomas Guyet - Agrocampus, IRISA France
    • Vincent Lemaire - Orange Labs, France
    • Simon Malinowski - Université de Rennes 1, IRISA, France
    • Romain Tavenard - Université de Rennes 2, COSTEL, France
    Website: https://project.inria.fr/aaltd19/ 🔗
  • MACLEAN: MAChine Learning for EArth ObservatioN 🔗

    The huge amount of data currently produced by modern Earth Observation (EO) missions has raised up new challenges for the Remote Sensing communities. EO sensors are now able to offer (very) high spatial resolution images with revisit time frequencies never achieved before considering different kind of signals, e.g., multi-(hyper)spectral optical, radar, LiDAR and Digital Surface Models. In this context, modern machine learning techniques can play a crucial role to deal with such amount of heterogeneous, multi-scale and multi-modal data. Some examples of techniques that are gaining attention in this domain include deep learning, domain adaptation, semi-supervised approach, time series analysis and active learning. Even though the use of machine learning and the development of ad-hoc techniques are gaining increasing popularity in the EO domain, we can witness that a significant lack of interaction between domain experts and machine learning researchers still exists. The objective of this workshop is to supply an international forum where machine learning researchers and domain-experts can meet each other, in order to exchange, debate and draw short and long term research objectives around the exploitation and analysis of EO data via Machine Learning techniques. Among the workshop’s objectives, we want to give an overview of the current machine learning researches dealing with EO data, and, on the other hand, we want to stimulate concrete discussions to pave the way to new machine learning frameworks especially tailored to deal with such data.

    • Thomas Corpetti - thomas.corpetti[at]irisa.fr
    • Dino Ienco - dino.ienco[at]irstea.fr
    • Roberto Interdonato - roberto.interdonato[at]cirad.fr
    • Minh-Tan Pham - minh-tan.pham[at]irisa.fr
    • Sébastien Lefèvre - sebastien.lefevre[at]irisa.fr
    Website: https://mdl4eo.irstea.fr/maclean-machine-learning-for-earth-observation/ 🔗
  • Automating Data Science 🔗

    Data science is concerned with the extraction of knowledge and insight, and ultimately societal or economic value, from data. It complements traditional statistics in that its object is data as it presents itself in the wild (often complex and heterogeneous, noisy, loosely structured, biased, etc.), rather than well structured data sampled in carefully designed studies. It also has a strong computer science focus, and is related to popular areas such as big data, machine learning, data mining and knowledge discovery. It is therefore highly relevant to the ECMLPKDD community. Data science is becoming increasingly important with the abundance of big data, while the number of skilled data scientists is lagging. This has raised the question as to whether it is possible to automate data science in several contexts. First, from an artificial intelligence perspective, it is interesting to investigate whether (data) science (or portions of it) can be automated, as it is an activity currently requiring high levels of human expertise. Second, the field of machine learning has a long-standing interest in applying machine learning at the meta-level, in order to obtain better machine learning algorithms, yielding recent successes in automated parameter tuning, algorithm configuration and algorithm selection. Third, there is an interest in automating not only the model building process itself (cf. the Automated Statistician) but also in automating the preprocessing steps (data wrangling) and the postprocessing steps (model deployment, monitoring and maintenance).

    • Tijl De Bie (UGent, Belgium)
    • Luc De Raedt (KU Leuven, Belgium)
    • Jose Hernandez-Orallo (Universitat Politecnica de Valencia, Spain)
    Website: https://sites.google.com/view/autods 🔗
  • The Fourth Workshop on Data Science for Social Good 🔗

    The aim of the workshop is to present applications of Data Science to Social Good, or else that take into account social aspects of Data Science methods and techniques. Every domain shall be considered, particularly if aligned with the UN Sustainable Development Goals. We also want to attract proposals of data-related projects that are looking for collaborators

    • Ricard Gavalda (UPC BarcelonaTech, Spain), gavalda[at]cs.upc.edu
    • Irena Koprinska (University of Sydney, Australia), irena.koprinska[at]sydney.edu.au
    • Joao Gama (University of Porto, Portugal), jgama[at]fep.up.pt
    Website: https://sites.google.com/view/ecmlpkddsogood2019 🔗
  • Advances in managing and mining large evolving graphs (third edition) 🔗

    The aim of the workshop called Managing and mining Large Evolving Graphs (LEG) is to bring together active scholars and practitioners of dynamic graphs. Graph models and algorithms are ubiquitous of a large number of application domains, ranging from transportation to social networks, semantic web, or data mining. However, many applications require graph models that are time dependent. For example, applications related to urban mobility analysis employ a graph structure of the underlying road network where the traffic density and speed continuously change over time. Therefore, the time a moving object takes to cross a path segment typically depends on the starting instant of time. This dynamicity makes it more challenging to mine temporal and graph patterns, yet this task is essential to study such structures. The same holds in other contexts, such as social networks. In this workshop, we aim to discuss the problem of mining large evolving graphs, since there are many real world applications deal with a large volumes of such data. Managing and analysing large evolving graphs is very challenging since this requires sophisticated methods and techniques for creating, storing, accessing and processing such graphs in a distributed environment, because centralized approaches do not scale in a Big Data scenario. Contributions will clearly point out answers to one of these challenges focusing on large-scale graphs.

    Organizers: Website: https://leg-ecmlpkdd19.loria.fr/ 🔗
  • Data and Machine Learning Advances with Multiple Views 🔗

    Multiview machine learning occurs whenever a model has to be learned to process tasks over data coming from different description spaces. For example, medical diagnosis might use various types (aka views) of examinations (MRI, ECG, blood analysis, etc.) in order to take a decision. These views are supposed to carry different types of information regarding the learning task, which means that these views reveal different types of patterns regularities. The workshop aims at bringing together people interested with multi-view learning, both from dataset providers to researchers in machine learning. Such a way, researchers could easily have the opportunity to inspect the reality of some true learning problems related to multi-view learning, meanwhile providers of natural multi-viewed data could get aware of the many current or potential solutions to address their learning tasks. From this perspective, in addition to a usual session of talks and posters, we propose to organise a tiny challenge – a hackathon – on real mutiview data, which could be provided by participants, in order to highlight a synergy during the workshop.

    • Stéphane Ayache
    • Cécile Capponi
    • Rémi Emonet
    • Usabelle Guyon
    Website: https://damvl.lis-lab.fr 🔗
  • Workshop on Data Integration and Applications 🔗

    The goal of the Data Integration and Applications (DINA) workshop is to bring together computer scientists with researchers from other domains and practitioners from businesses and governments to present and discuss current research directions on multi-source data integration and its application. The workshop will provide a forum for original high-quality research papers on record linkage, data integration, population informatics, mining techniques of integrated data, and applications, as well as multidisciplinary research opportunities.

    • Luiza Antonie, University of Guelph, Canada
    • Peter Christen, The Australian National University, Australia
    • Erhard Rahm, University of Leipzig, Germany
    • Osmar Zaïane, University of Alberta, Canada
    Website: https://sites.google.com/view/dina2019/ 🔗



  • Constraint Learning 🔗

    Constraints are ubiquitous in artificial intelligence, machine learning, and operations research. They appear in purely logical problems such as propositional satisfiability, constraint satisfaction problems, constraint programming and full-fledged constraint optimization. Constraint learning is required when the structure and/or the parameters of the target constraint satisfaction (or optimization) problem are not known in advance, and must be learned. Potential sources of supervision include offline data and other oracles, e.g. human domain experts and decision makers. This tutorial provides a gentle and coincise introduction to the diverse area of constraint learning, covering different formalisms and learning methods, highlighting the commonalities and differences between them. It will span learning hard and soft constraints, as well as learning in offline (batch) and in interactive settings. We will discuss relevant applications, including programming-by-example and structured prediction.

    • Luc De Raedt, Department of Computer Science, KU Leuven, Belgium
    • Andrea Passerini, Department of Information Engineering and Computer Science, University of Trento, Italy
    • Stefano Teso, Department of Computer Science, KU Leuven, Belgium
    Website: https://dtai.cs.kuleuven.be/events/tutorial-constraint-learning-ecml-pkdd-2019 🔗
  • The ins and outs of reviewing: the good, the bad, and the ugly 🔗

    Every scientist faces the reviewing process sooner or later. The first contact with it is typically as an author, later also as a reviewer, and possibly as area chair, editor, … The quality of conferences and journals heavily relies on a high-quality reviewing process. A high-quality reviewing process is in your interest, as an author, but is impossible without your commitment as a reviewer. This tutorial intends to teach about reviewing and being reviewed. It offers insight into the reviewing process: the different steps, who is involved and what is their role, how long do the different steps typically take, ... It offers advice to (prospective) authors, reviewers, area chairs, program chairs, editors. The tutorial is meant to be interactive: active involvement of the audience will be solicited. The prospective structure is as follows:

    • Part 1: The reviewing process
    • Part 2: Dealing with reviews as an author
    • Part 3: Reviewing papers
    • Part 4: Organizing / leading the reviewing process
    • Part 5: Open discussion

    • Hendrik Blockeel
    • Jesse Davis
    Website: not yet available 🔗
  • Machines Who Imagine: Going Beyond Data Science 🔗

    Humans exhibit a strong predisposition to imagine — to mentally transcend time, place, and circumstance — from an early age, an ability that is at the heart of all creative human activities, including art, literature, poetry, science, and technology. The primary goal of this half day tutorial is to introduce a new field of study in machine learning (ML) called imagination science. In contrast to data science, which concerns itself largely with answering “What is?” questions, imagination science addresses a much broader range of questions, including “What if”? and “Why?”, that concern the planning of causal interventions and reasoning about impossible counterfactual situations. The tutorial will outline the key challenges in automating imagination, discuss connections between imagination and ongoing research in various areas, such as causality, deep learning, and transfer learning, and show why automation of imagination may transform ML in the coming decades.

    • Sridhar Mahadevan, Adobe Research and Univ. of Massachusetts, Amherst
    Website: https://people.cs.umass.edu/~mahadeva/ECML_2019_Tutorial 🔗
  • Data Mining and Machine Learning using Constraint Programming Languages - An Overview and Future Directions 🔗

    In recent years it has been realized that many data mining and machine learning problems, especially unsupervised ones, can be formalized as discrete constraint satisfaction and optimization problems. This tutorial provides a detailed overview of the use of constraint solving technology, such as constraint programming, SAT solvers, and MIP solvers, to solve these problems. It will show how learning and mining problems that involve additional constraints, can be solved elegantly in these frameworks.

    • Ian Davidson
    • Tias Guns
    • Siegfried Nijssen
    Website: https://sites.uclouvain.be/cp4dm/tutorial/ecmlpkdd19/ 🔗


  • Adaptive Influence Maximization 🔗

    Information diffusion and social influence are more and more present in today's Web ecosystem. Having algorithms that optimize the presence and message diffusion on social media is indeed crucial to all actors (media companies, political parties, corporations, etc.) who advertise on the Web. Motivated by the need for effective viral marketing strategies, influence estimation and influence maximization have therefore become important research problems, leading to a plethora of methods. However, the majority of these methods are non-adaptive, and therefore not appropriate for scenarios in which influence campaigns may be ran and observed over multiple rounds, nor for scenarios which cannot assume full knowledge over the diffusion networks and the ways information spreads in them. In this tutorial we intend to present the recent research on adaptive influence maximization, which aims to address these limitations. This can be seen as a particular case of the influence maximization problem (where seeds in a social graph are selected to maximize information spread), one in which the decisions are taken as the influence campaign unfolds, over multiple rounds, and where knowledge about the graph topology and the influence process may be partial or even entirely missing. This setting, depending on the underlying assumptions, leads to variate and original approaches and algorithmic techniques, as we have witnessed in recent literature. We will review the most relevant research in this area, by organizing it along several key dimensions, and by discussing the methods' advantages and shortcomings, along with open research questions and the practical aspects of their implementation.

    • Bogdan Cautis: LRI, Université Paris-Sud, France (bogdan.cautis[at]u-psud.fr)
    • Silviu Maniu: LRI, Université Paris-Sud, France (silviu.maniu[at]lri.fr)
    • Nikolaos Tziortziotis: Tradelab and LRI, Université Paris-Sud, France (ntziorzi[at]gmail.com)
    Website: https://sites.google.com/view/aim-tutorial/home 🔗
  • On Ordered Sets in Pattern Mining 🔗

    Pattern mining is the task of discovering interpretable and actionable patterns in data. The aim of this tutorial is to discuss the general task of pattern mining and related problems from the order-theoretic point of view. This allows one to provide a unified view defining a pattern mining task, as well as the underlying pattern space. To achieve this, we start by presenting a quite simple, yet general, framework, dubbed pattern setup. We instantiate subsequently the framework on various pattern mining problems on different pattern languages. Next, we explore different algorithms to explore pattern search spaces. Finally, we investigate some open problems. Our target audience is researchers and postgraduates interested in pattern mining, knowledge discovery, and related fields.

    • Aimene Belfodil . aimene.belfodil[at]insa-lyon.fr
    • Mehdi Kaytoue . mehdi.kaytoue[at]insa-lyon.fr
    • Sergei O. Kuznetsov . skuznetsov[at]{hse, yandex}.ru
    • Amedeo Napoli . amedeo.napoli[at]loria.fr
    Website: https://belfodilaimene.github.io/pattern-setups-tutorial/ 🔗
  • Machine Learning for Automatic Word Problem Solving 🔗

    In the recent past, and more specifically over the last five years, there has been a growing interest in applying machine learning techniques for understanding and solving mathematical word problems. Advances in this rapidly evolving field that seek to address an interesting but sophisticated task is yet to receive more widespread attention within the broader ML community. This tutorial aims to summarize this flourishing and growing field, focusing on two major trends of approaching this problem; viz., building probabilistic models and designing sequence-to-sequence (seq2seq) architectures. We will start by providing a concrete specification of the task and historical context comprising early approaches to the problem. We then cover the recent research within this field using our categorization across the two major families as alluded to earlier, and then offer perspectives on general trends and future evolution of the area.

    • Sowmya S Sundaram
    • Savitha Sam Abraham
    • Deepak P
    Website: https://sites.google.com/smail.iitm.ac.in/tutorial-ai-for-word-problems/ 🔗
  • Scalable Deep Learning: from theory to practice 🔗

    A fundamental task for artificial intelligence is learning. Deep Neural Networks have proven to cope perfectly with all learning paradigms, i.e. supervised, unsupervised, and reinforcement learning. Nevertheless, traditional deep learning approaches make use of cloud computing facilities and do not scale well to autonomous agents with low computational resources. Even in the cloud they suffer from computational and memory limitations and cannot be used to model properly large physical worlds for agents which assume networks with billion of neurons. These issues were addressed in the last few years by the emerging topics of scalable and efficient deep learning. The tutorial covers these topics focusing on theoretical advancements, practical applications, and hands-on experience.

    • Elena Mocanu
    • Decebal Constantin Mocanu
    Website: https://sites.google.com/view/sdl-ecmlpkdd-2019-tutorial 🔗



  • Interactive Adaptive Learning 🔗

    The tutorial and workshop aims at discussing techniques and approaches for optimising the whole learning process, including the interaction with human supervisors, processing systems, and includes adaptive, active, semi-supervised, and transfer learning techniques, and combinations thereof in interactive and adaptive machine learning systems. Our objective is to bridge the communities researching and developing these techniques and systems in machine learning and data mining.

    • Georg Krempl (Utrecht University, Netherlands)
    • Vincent Lemaire (Orange Labs, France)
    • Daniel Kottke (University of Kassel, Germany)
    • Andreas Holzinger (Medical University Graz, Austria)
    • Adrian Calma (Darwin, USA)
    Website: https://p.ies.uni-kassel.de/ial2019/ 🔗
  • IoT Stream for Data Driven Predictive Maintenance 🔗

    Maintenance is a critical issue in the industrial context for the prevention of high costs or injures. The emerging technologies of Industry 4.0 empowered data production and exchange which lead to new concepts and methodologies exploitation for maintenance. Intensive research effort in data driven Predictive Maintenance (PdM) has been producing encouraged outcomes. Therefore, the main objective of this workshop is to raise awareness of research trends and promote interdisciplinary discussion in this field.

    • Rita Ribeiro, Laboratory of Artificial Intelligence and Decision Support, University of Porto, Porto
    • Albert Bifet, Telecom-ParisTech; Paris, France
    • João Gama, Laboratory of Artificial Intelligence and Decision Support, University of Porto, Porto, Portugal
    • Anders Holst, RISE SICS
    • Sepideh Pashami, Halmstad University
    Website: https://abifet.wixsite.com/iotstream2019 🔗


  • XKDD Tutorial and XKDD - AIMLAI Workshop 🔗

    The purpose of AIMLAI-XKDD (Advances in Interpretable Machine Learning and Artificial Intelligence & eXplainable Knowledge Discovery in Data Mining), is to encourage principled research that will lead to the advancement of explainable, transparent, ethical and fair data mining, machine learning, artificial intelligence. AIMLAI-XKDD is an event organized into two moments: a tutorial to introduce audience to the topic, and a workshop to discuss recent advances in the research field. The tutorial will provide a broad overview of the state of the art and the major applications for explainable and transparent approaches. Likewise it will highlight the main open challenges. The workshop will seek top-quality submissions addressing uncovered important issues related to explainable and interpretable data mining and machine learning models. Papers should present research results in any of the topics of interest for the workshop as well as application experiences, tools and promising preliminary ideas. AIMLAI-XKDD asks for contributions from researchers, academia and industries, working on topics addressing these challenges primarily from a technical point of view, but also from a legal, ethical or sociological perspective.

    • Riccardo Guidotti, KDD Lab, ISTI-CNR, Italy
    • Pasquale Minervini, University College London, UK
    • Anna Monreale, KDD Lab, University of Pisa, Italy
    • Salvatore Rinzivillo, KDD Lab, ISTI-CNR, Italy
    • Adrien Bibal, University of Namur, Belgium
    • Tassadit Bouadi, University of Rennes/IRISA, France
    • Benoît Frénay, University of Namur, Belgium
    • Luis Galárraga, Inria/IRISA, France
    • Stefan Kramer, Universität Mainz, Germany
    • Ruggero G. Pensa, University of Turin, Italy
    Website: https://kdd.isti.cnr.it/xkdd2019/ 🔗


In case you have further questions, please do not hesitate to contact the Workshop and Tutorial Chairs (Peggy Cellier and Kurt Driessens) at wt_chairs[at]ecmlpkdd2019.org.