ABOUT ECML PKDD

The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases will take place in Skopje, Macedonia, September 18–22, 2017.

This event is the premier European machine learning and data mining conference and builds upon a very successful series of 27 ECML and 20 PKDD conferences, which have been jointly organized for the past 15 years.

Registration Fees

The conference registration fee (student and regular) includes admission to all sessions of the main conference, workshops and tutorials, access to on-line articles during the conference and on-line post proceedings after the conference, lunches, refreshments, and social events.

Please register here.

More information on how to register is available here.

Early Registration - before July 31, 2017 -

  • Student 370
  • Regular 450
  • Accompanee 100

Mid Registration - before August 31, 2017 -

  • Student 470
  • Regular 550
  • Accompanee 150

Late Registration - after August 31, 2017 -

  • Student 570
  • Regular 650
  • Accompanee 150

Invited Speakers

Pierre-Philippe Mathieu

ESA/ESRIN, EO Science,
Applications and New Technologies

Enabling a smarter planet
with Earth Observation

Frank Hutter

University of Freiburg

Towards end-to-end
learning & optimization

Alex Graves

Google DeepMind

Frontiers in Recurrent
Neural Network Research

Cordelia Schmid

INRIA

Automatic Understanding of
the Visual World

Discovery Challenges

The objective of this challenge is to bring closer the Machine Learning and Remote Sensing communities to work on such kind of data. The Machine Learning community has the opportunity to validate and test their approaches on real world data in an application context that is getting more and more attention due to the increasing availability of SITS data while, this challenge offers to the Remote Sensing experts a way to discover and evaluate new data mining and machine learning methods to deal with SITS data.

The challenge involves a multi-class single label classification problem where the examples to classify are pixels described by the time series of satellite images and the prediction is related to the land cover of associated to each pixel. A more detailed description follows.

Organizers:
Dino Ienco, UMR TETIS - IRSTEA, Montpellier, France
Raffaele Gaetano, UMR TETIS - CIRAD, Montpellier, France


Challenge web page

In this challenge, we focus our attention on photovoltaic (PV) power plants, due to their wide distribution in Europe. During the last years, the forecast of PV energy production has received significant attention since photovoltaics are becoming a major source of renewable energy for the world. Forecast may apply to a single renewable power generation system, or refer to an aggregation of large numbers of systems spread over an extended geographic area.

Organization:
Roberto Corizzo, University of Bari, Italy


Challenge web page

The Mars Express Power Challenge focuses on the difficult problem of predicting the thermal power consumption. More than six Earth years of Mars Express telemetry are made available and you are challenged to predict the thermal subsystem power consumption on the following 26 Earth months. If successful, the winning method will be integrated in the new tool helping operators of the Mars Express Orbiter to deliver science data for a longer period of time.

Organization:
ESA Data Analytics Team for Operations
Mars Express Flight Control Team


Challenge web page

Workshops & Tutorials

Monday, September 18th, 2017

Workshops

Abstract:
Sports Analytics has been a steadily growing and rapidly evolving area over the last decade, both in US professional sports leagues and in European football leagues. The majority of techniques used in the field so far are statistical. However, there has been growing interest in the Machine Learning and Data Mining community about this topic as this setting is interesting, challenging and offers new sources of data. The workshop concerns all aspects of applying machine learning and data mining techniques for sports problems such as match strategy, tactics, and analysis; player acquisition, player valuation, and team spending; injury prediction and prevention; match outcome and league table prediction; and tournament design and scheduling among others.

Organizers:
Jesse Davis, KU Leuven, Belgium
Mehdi Kaytoue, INSA Lyon, France
Albrecht Zimmermann, University of Caen, France

Workshop web page

Abstract:
In the era of Big Data, every single user of our hyper-connected world leaves behind a myriad of digital breadcrumbs while performing her daily activities. In this context personal data analytics and individual privacy protection are the key elements to leverage nowadays services to a new type of systems. The availability of personal analytics tools able to extract hidden knowledge from individual data while protecting the privacy right can help the society to move from organization-centric systems to user-centric systems, where the user is the owner of her personal data and is able to manage, understand, exploit, control and share her own data and the knowledge deliverable from them in a completely safe way.

Organizers:
Serge Abiteboul, Inria, ENS Paris, France
Riccardo Guidotti, KDDLab, ISTI-CNR Pisa, Italy
Anna Monreale, University of Pisa, Italy
Dino Pedreschi, University of Pisa, Italy

Workshop web page

Abstract:
This workshops aims to attract papers presenting applications of Data Science to Social Good, or else that take into account social aspects of Data Science methods and techniques. Application domains should be as varied as possible. The novelty of the application and its social impact will be major selection criteria.

Organizers:
Ricard Gavaldà, UPC BarcelonaTech, Spain
Irena Koprinska, University of Sidney, Australia
Stefan Kramer, JGU Mainz, Germany

Workshop web page

Abstract:
Reinforcement Learning (RL) has achieved many successes over the years in training autonomous agents to perform simple tasks. However, one of the major remaining challenges in RL is scaling it to high-dimensional, real-world applications.

Although many works have already focused on strategies to scale-up RL techniques and to find solutions for more complex problems with reasonable successes, many issues still exist. This workshop encourages to discuss diverse approaches to accelerate and generalize RL, such as the use of approximations, abstractions, hierarchical approaches, and Transfer Learning.

Organizers:
Felipe Leno da Silva, University of São Paulo, Brazil
Ruben Glatt, University of São Paulo, Brazil

Workshop web page

Abstract:
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.

Organizers:
Ilaria Bordino, UniCredit, R& D Dept., Italy
Guido Caldarelli, IMT Institute for Advanced Studies Lucca, Italy
Fabio Fumarola, UniCredit, R& D Dept., Italy Francesco Gullo, UniCredit, R& D Dept., Italy
Tiziano Squartini, IMT Institute for Advanced Studies Lucca, Italy

Workshop web page

Abstract:
Network science, network analysis, and network mining are new scientific topics that emerged in recent years and are growing quickly. Instead of studying the properties of entities, network science focus on the interaction between these entities. The tremendous quantity of relational data that become available (Online Social Networks, cell phones, the Internet and the Web, trip datasets, etc.) encourage new research on the topic.

Organizers:
Giulio Rossetti, KDD Laboratory, ISTI-CNR Pisa, Italy
Rémy Cazabet, LIP6, CNRS, Sorbonne Universités, France
Letizia Milli, Computer Science Department - University of Pisa, Italy

Workshop web page

Abstract:
The aim of this workshop called Large-Scale Time Dependent Graphs (TD-LSG) 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. Indeed, the nature of such networks are spatiotemporal. Therefore, the time a moving object takes to cross a path segment typically depends on the starting instant of time. So, we call time-dependent graphs, the graphs that have this spatiotemporal feature.

Organizers:
Sabeur Aridhi, University of Lorraine, France
José Fernandes de Macedo, Universidade Federale do Ceara, Fortaleza, Brazil
Engelbert Mephu Nguifo, LIMOS, Blaise Pascal University, France
Karine Zeitouni, DAVID, Université de Versailles Saint-Quentin, France

Workshop web page

Combined Workshops & Tutorials

Abstract:
The volume of data is rapidly increasing due to the development of the technology of information and communication. This data comes mostly in the form of streams. Learning from this ever-growing amount of data requires flexible learning models that self-adapt over time. In addition, these models must take into account many constraints: (pseudo) real-time processing, high-velocity, and dynamic multi-form change such as concept drift and novelty. This workshop welcomes novel research about learning from data streams in evolving environments. It will provide the researchers and participants with a forum for exchanging ideas, presenting recent advances and discussing challenges related to data streams processing. It solicits original work, already completed or in progress. Position papers are also considered. This workshop is combined with a tutorial treating the same topic and will be presented in the same day.

Organizers:
Moamar Sayed-Mouchaweh, Computer Science and Automatic Control Labs, High Engineering School of Mines, Douai
Albert Bifet, Telecom-ParisTech; Paris, France
Hamid Bouchachia, Department of Computing & Informatics, University of Bournemouth, Bournemouth, UK
João Gama, Laboratory of Artificial Intelligence and Decision Support, University of Porto, Porto, Portugal
Rita Ribeiro, Laboratory of Artificial Intelligence and Decision Support, University of Porto, Porto, Portugal

Workshop and tutorial web page

Abstract:
This workshop on interactive adaptive learning 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. Therefore we welcome contributions that present a novel problem setting, propose a novel approach, or report experience with the practical deployment of such a system and raise unsolved questions to the research community.

Organizers:
Georg Krempl, University Magdeburg, Germany
Vincent Lemaire, Orange Labs, France
Robi Polikar, Rowan University, USA
Bernhard Sick, University of Kassel, Germany
Daniel Kottke, University of Kassel, Germany
Adrian Calma, University of Kassel, Germany

Workshop and tutorial web page

Tutorials

Abstract: Graph mining is an important research area with a plethora of practical applications. Core decomposition of networks is a fundamental operation strongly related to more complex mining tasks such as community detection, dense subgraph discovery, identification of influential nodes, network visualization, text mining, just to name a few. In this tutorial, we will present in detail the concept and properties of core decomposition in graphs, the associated algorithms for its efficient computation and important cross-disciplinary applications that benefit from it.

Organizers:
Fragkiskos D. Malliaros, UC San Diego La Jolla, USA
Apostolos N. Papadopoulos, Aristotle University of Thessaloniki, Thessaloniki, Greece
Michalis Vazirgiannis, Ecole Polytechnique Palaiseau, France

Tutorial web page

Abstract: Graph mining is an important research area with a plethora of practical applications. Core decomposition of networks is a fundamental operation strongly related to more complex mining tasks such as community detection, dense subgraph discovery, identification of influential nodes, network visualization, text mining, just to name a few. In this tutorial, we will present in detail the concept and properties of core decomposition in graphs, the associated algorithms for its efficient computation and important cross-disciplinary applications that benefit from it.

Organizers:
Fragkiskos D. Malliaros, UC San Diego La Jolla, USA
Apostolos N. Papadopoulos, Aristotle University of Thessaloniki, Thessaloniki, Greece
Michalis Vazirgiannis, Ecole Polytechnique Palaiseau, France

Tutorial web page

Friday, September 22nd, 2017

Workshops

Abstract:
Modern automatic systems are able to collect huge volumes of data, often with a complex structure (e.g. multi-table data, XML data, web data, time series and sequences, graphs and trees). 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 analysing 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.

Organizers:
Annalisa Appice, 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, Department of Computer Science, University of North Carolina, Charlotte, USA

Workshop web page

Abstract:
Many real-world data-mining applications involve obtaining and evaluating predictive models using data sets with strongly imbalanced distributions of the target variable. Frequently, the least-common values are associated with events that are highly relevant for end users. This problem has been thoroughly studied in the last decade with a specific focus on classification tasks. However, the research community has started to address this problem within other contexts. It is now recognized that imbalanced domains are a broader and important problem posing relevant challenges for both supervised and unsupervised learning tasks, in an increasing number of real world applications. This workshop invites inter-disciplinary contributions to tackle the problems that many real-world domains face today. With the growing attention that this problem has collected, it is crucial to promote its development and to tackle its theoretical and application challenges.

Workshop web page

Abstract:
Deep Learning is beginning to exert a disruptive impact for functional genomics, with applications of high industrial and ethical relevance in pharmacogenomics and toxicogenomics. Moreover, in less than one year, Deep Learning has emerged with solutions in diagnostic imaging and pathology that have reached best human expertise, as for the success in games. Examples in miRNA prediction already demonstrated the potential for deriving implicit features with high predictive accuracy, and novel methods for genomewide association studies and prediction of molecular traits following suite are appearing both as scientific initiatives as well as key technologies of AI startups. Following the success of DLPM2016, also colocated with ECML/PKDD in 2016, we thus wish to discuss about the best options for the adoption of deep learning models, both for improved accuracy as well as for better biomedical understanding. Questions such as end-to-end modeling from structure to functionality and biological impact as well as architectures for integration of genotype, expression and epigenetics would be of immediate interest for the workshop. Further topics of interest are described in the call.

Organizers:
Bertram Müller-Myhsok, University of Liverpool, Liverpool, UK and Max Planck Institute of Psychiatry, Munich, Germany
Cesare Furlanello, Fondazione Bruno Kessler - FBK Trento, Italy

Contact: dlpm2017@fbk.eu

Workshop web page

Abstract:
DMNLP’17 will be the fourth edition of the Data Mining and Natural Language Processing (DMNLP) workshop. The workshop will favor the use of symbolic methods. Indeed, statistical and machine learning methods (CRF, SVM, Naive Bayes) holds a predominant position in NLP researches and ”may have been too successful (...) as there is no longer much room for anything else”. They have proved their effectiveness for some tasks but one major drawback is that they do not provide human readable models. By contrast, symbolic machine learning methods are known to provide more human-readable model that could be an end in itself (e.g., for stylistics) or improve, by combination, further methods including numerical ones. Research in Data Mining has progressed significantly in the last decades, through the development of advanced algorithms and techniques to extract knowledge from data in different forms. In particular, for two decades Pattern Mining has been one of the most active field in Knowledge Discovery.

Organizers:
Peggy Cellierm, INSA Rennes, IRISA (UMR 6074), Rennes, France
Thierry Charnois, Université de Paris 13, LIPN (UMR 7030), France
Andreas Hotho, University of Kassel, Germany
Marie-Francine Moens: Katholieke Universiteit, Leuven, Belgium
Stan Matwin, Dalhousie University, Canada
Yannick Toussaint, INRIA, LORIA (UMR 7503), 54506 Vandoeuvre-les-Nancy, France

Workshop web page

Abstract:
Security and privacy aspects of data analytics become of central importance in many application areas. New legislation also pushes companies and research communities to address challenges of privacy-preserving data analytics. In our data mining community, questions about data privacy and security have been predominantly approached from the perspective of k-anonymity and differential privacy.

Organizers:
Mykola Pechenizkiy, Eindhoven University of Technology, Netherlands
Stefan Kramer, University of Mainz, Germany
Niek J. Bouman, Eindhoven University of Technology, Netherlands

Workshop web page

Abstract:
The recent technological advances on telecommunications create a new reality on mobility sensing. Nowadays, we live in an era where ubiquitous digital devices are able to broadcast rich information about human mobility in real-time and at high rate. Such fact exponentially increased the availability of large-scale mobility data which has been popularized in the media as the new currency, fueling the future vision of our smart cities that will transform our lives. The reality is that we just began to recognize significant research challenges across a spectrum of topics. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders on build knowledge discovery pipelines over such data sources. However, such availability also raise privacy issues that must be considered by both industrial and academic stakeholders on using these resources.

Organizers:
Luis Moreira-Matias – NEC Laboratories Europe, Germany
Roberto Trasarti, KDD Lab ISTI-CNR, Pisa, Italy
Rahul Nair, IBM Research Ireland

Workshop web page

Abstract:
Climate change, the depletion of natural resources and rising energy costs have led to an increasing focus on renewable sources of energy. A lot of research has been devoted to the technologies used to extract energy from these sources; however, equally important is the storage and distribution of this energy in a way that is efficient and cost effective. Achieving this would generally require integration with existing energy infrastructure.

Organizers:
Wei Lee Woon, Masdar Institute, United Arab Emirates
Zeyar Aung, Masdar Institute, United Arab Emirates
Oliver Kramer, University of Oldenburg, Germany
Stuart Madnick, Massachusetts Institute of Technology, USA

Workshop web page

Combined Workshops & Tutorials

Workshop abstract:
This workshop will provide a platform for discussing the recent developments in the area of algorithm selection and configuration, which arises in many diverse domains, such as machine learning, data mining, optimization and automated reasoning. Algorithm selection and configuration are increasingly relevant today. Researchers and practitioners from all areas of science and technology face a large choice of parameterized machine learning algorithms, with little guidance as to which techniques to use in a given application context. Moreover, data mining challenges frequently remind us that algorithm selection and configuration are crucial in order to achieve cutting-edge performance, and drive industrial applications.

Workshop web page

Tutorial abstract:
This tutorial will introduce and discuss state of the art methods in meta-learning, algorithm selection, and algorithm configuration, which are increasingly relevant today. Researchers and practitioners from all areas of science and technology face a large choice of parameterized machine learning algorithms, with little guidance as to when and how to use which technique. Data mining challenges frequently remind us that algorithm selection and configuration play a crucial role in achieving cutting-edge performance, and are indispensible in industrial applications.

Tutorial web page

Organizers:
Pavel Brazdil, LIAAD Inesc Tec., Portugal
Joaquin Vanschoren, Eindhoven University of Technology, Netherlands
Holger H. Hoos, Universiteit Leiden, Netherlands
Frank Hutter, University of Freiburg, Germany

Tutorials

Abstract:
Global fossil databases have been growing rapidly in the last decade. They aggregate and accumulate findings and knowledge that palaeobiologists acquired over many years. These datasets are big data in their essence - compiled from different sources, to an extent subjective, include specific biases and uncertainties, data sparseness and quality varies over time and space. In addition, to understand relations between organisms and climate high volume and large velocity satellite observations some into play that require scalability in computing. Databases of this kind offer an excellent ground for interdisciplinary machine learning research. This tutorial will outline research questions that could be addressed using computational methods, discuss characteristics of fossil data and computational tasks for machine learning and data mining, overview existing computational approaches, and discuss what more could be done from the machine learning and data mining perspective.

Organization:
Indrė Žliobaitė, University of Helsinki, Finnland

Tutorial web page

Abstract:
Deep Learning methods have become ubiquitous for computer vision tasks. This tutorial will focus on recent advances in deep learning for vision applications in robotics and autonomous vehicles. The tutorial will start with basic Deep Learning techniques and will highlight state-of-the-art methods in the three major topics in computer vision: classification, detection and segmentation. Then the tutorial will continue with more concrete methods and their applications, e.g. in scene understanding, 3D analysis, perception for robotics and autonomous driving. The goal of the tutorial is to focus on relevant techniques, which are of significant impact to real-world applications, and which will benefit the broader Machine Learning community.

Organization:
Anelia Angelova, Google Research / Google Brain
Fidler, University of Toronto

Tutorial web page

Call for papers

We invite submissions for the journal track of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) 2017. The journal track of the conference is implemented in partnership with the Machine learning journal and the Data mining and Knowledge Discovery journal. The conference provides an international forum for the discussion of the latest high-quality research results in all areas related to machine learning, data mining, and knowledge discovery.

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Submissions are solicited for the 2017 edition of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2017). The conference provides an international forum for the discussion of the latest high-quality research results in all areas related to machine learning and knowledge discovery in databases and other innovative application domains. The 2017 conference will take place in Skopje, Macedonia, 18‐22 September 2017.

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We solicit submissions for demos for ECML PKDD 2017 in Skopje, Macedonia. Submissions must describe working systems and be based on state-of-the-art machine learning and data mining technology. These systems may be innovative prototype implementations or mature systems that use machine learning techniques and knowledge discovery processes in a real setting. Systems that use basic statistics are not acceptable. Commercial software systems are not acceptable.

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The goal of the Nectar Track, started in 2012, is to offer conference attendees a compact overview of recent scientific advances at the frontier of machine learning and data mining with other disciplines, as published in related conferences and journals. For researchers from the other disciplines, the Nectar Track offers a place to present their work to the ECMLPKDD community and to raise the community's awareness of data analysis results and open problems in their field. We invite senior and junior researchers to submit summaries of their own work published in neighboring fields, such as (but not limited to) artificial intelligence, big data analytics, bioinformatics, cyber security, games, computational linguistics, natural language processing, information retrieval, computer vision and image analysis, geoinformatics, health informatics, database theory, human computer interaction, information and knowledge management, robotics, pattern recognition, statistics, social network analysis, theoretical computer science, uncertainty in AI, network science, complex systems science, and computationally oriented sociology, economy and biology, as well as critical data science/studies.

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The Applied Data Science Track of ECML-PKDD 2017 follows the success of the previous years with a separate Program Committee and a separate Proceedings volume. The track aims to bring together participants from academia, industry, governments and NGOs (non-governmental organizations) in a venue that highlights practical and real-world studies of machine learning, knowledge discovery and data mining. This track wants to encourage mutually beneficial interactions between those engaged in scientific research and practitioners working to improve big data mining and large-scale machine learning analytics. Novel and practical ideas, open problems in Applied Data Science, description of application–specific challenges and unique solutions adopted in bridging the gap between research and practice are some of the relevant topics for this track.

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The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) includes a PhD Forum. The purpose of this forum is to provide a friendly environment for junior PhD students to exchange ideas and experiences with peers in an interactive atmosphere and to get constructive feedback from senior researchers in areas covered by the conference.

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Bi-weekly cut-off dates

31.7.2016
28.8.2016
25.9.2016
9.10.2016
23.10.2016
6.11.2016
20.11.2016
4.12.2016
18.12.2016
8.1.2017
22.1.2017
5.2.2017
19.2.2017
5.3.2017
19.3.2017

Important dates

  • Abstract submission deadline:
    Thursday, April 13, 2017
  • Paper submission deadline:
    Thursday, April 20, 2017
  • Author Rubuttal Period:
    Thursday, June 1 - Monday, June 5, 2017
    Tuesday, June 6th, 2017 12:00 noon CEST
    (June 6th, 2017 03:00 am Pacific Time Zone)
  • Author notification:
    Thursday, June 22, 2017
  • Camera ready submission:
    Thursday, July 6, 2017
    Friday, July 14, 2017

All deadlines expire on 23:59
Pacific Time Zone (UTC-8)

Important dates

  • Demo submission deadline:
    Thursday, May 11, 2017
  • Notification of acceptance:
    Thursday, June 22, 2017
  • Camera-ready paper due:
    Thursday, July 6, 2017
  • Presentation of the live demos:
    Tuesday, September 19, 2017
    Thursday, September 21, 2017

All deadlines expire on 23:59
Pacific Time Zone (UTC-8)

Important dates

  • Submission deadline:
    Thursday, May 18, 2017
  • Author notification:
    Thursday, June 22, 2017
  • Camera-ready paper due:
    Thursday, July 6, 2017

All deadlines expire on 23:59
Pacific Time Zone (UTC-8)

Important dates

  • Abstract submission deadline:
    Thursday, April 13, 2017
  • Paper Submission deadline:
    Thursday, April 20, 2017
  • Author notification:
    Thursday, June 22, 2017
  • Camera-ready paper due:
    Thursday, July 6, 2017

All deadlines expire on 23:59
Pacific Time Zone (UTC-8)

Important dates

  • Paper submission deadline:
    Thursday, June 15, 2017
    Friday, June 30, 2017
  • Author notification:
    Thursday, July 13, 2017
    Thursday, July 20, 2017
  • Camera-ready paper due:
    Thursday, August 3, 2017

All deadlines expire on 23:59
Pacific Time Zone (UTC-8)

EU Projects Forum

The EU Projects Forum at ECML PKDD 2017 is a novel initiative that encourages the dissemination of EU projects and their results to the ECML/PKDD conference participants and an opportunity for the ECML PKDD 2017 audience to learn about the European scientific success stories in the field. As a satellite event of ECML PKDD 2017, it is envisioned as a machine learning/data mining/big data/data science venue for gathering of ERC grantees, EU project consortia members, EU project officers, with the ECML PKDD participants including the researchers and the interested industrial participants.

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WE ARE GETTING THERE

Organizing Institutions