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

John Quackenbush

Dana-Farber Cancer Institute
and Harvard TH Chan School of Public Health

Using Networks to Link Genotype to Phenotype

Alex Graves

Google DeepMind

Frontiers in Recurrent Neural Network Research

Cordelia Schmid

INRIA

Automatic Understanding of the Visual World

Inderjit Dhillon

University of Texas at Austin

Multi-Target Prediction via Low-Rank Embeddings

TBA

TBA

TBA

TBA

TBA

TBA

Monday, September 18th, 2017

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.

Scaling-up RL methods has major implications on the research and practice of complex learning problems and will eventually lead to successful implementations in real-world applications.

This workshop intends to bridge the gap between conventional and scalable RL approaches. We aim to bring together resarchers working on different approaches to scale-up RL with the goal to solve more complex or larger scale problems. We intend to make this an exciting event for researchers worldwide, not only for the presentation of top quality papers, but also to spark the discussion of opportunities and challenges for future research directions.

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.

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.

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.

In the last years, we witnessed a shift from static network analysis to dynamic ones, i.e., the study of networks whose structure changes over time. As time goes by, all the perturbations which occur in the network topology due to the rise and fall of nodes and edges have repercussions on the network phenomena we are used to observing. As an example, evolution over time of social interactions in a network can play an important role in the diffusion of an infectious disease.

Nowadays, one of the most fascinating challenges is to analyze the structural dynamics of real world networks and how they impact on the processes which occur on them, i.e. the spreading of social influence and diffusion of innovations. Results in this field will enable a better understanding of important aspects of human behaviors as well as to a more detailed characterization of the complex interconnected society we inhabit. Since the last decades, diffusive and spreading phenomena were facilitated by the enormous popularity of the Internet and the evolution of social media that enable an unprecedented exchange of information. For this reason, understanding how social relationships unravel in these rapidly evolving contexts represents one of the most interesting fields of research. The purpose of the third edition of this workshop is to encourage research that will lead to the advancement of the social science in time-evolving networks.

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.

In this workshop, we aim to discuss the problem of mining large-scale time-dependent graphs, since there are many real world applications deal with a large volumes of spatio-temporal data (e.g. moving objects’ trajectories). Managing and analysing large-scale time-dependent 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:
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 with 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

Friday, September 22nd, 2017

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.

The purpose of the workshop is to bring together researchers and practitioners of data mining and machine learning interested in analysis of complex data, in order to promote and publish research in the field of complex pattern mining.

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.

Organizers:
Luís Torgo, University of Porto; LIAAD - INESC Tec, Portugal
Bartosz Krawczyk, Virginia Commonwealth University; Department of Computer Science,USA
Paula Branco, University of Porto; LIAAD - INESC Tec, Portugal
Nuno Moniz, University of Porto; LIAAD - INESC Tec, Portugal

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.

This event is intended to be a one-day workshop within ECML-PKDD 2017 including lectures and discussions on this very timely, pressing and active subject. We aim to create a connection between machine learning experts and leaders in the Precision Medicine initiatives in Europe and the USA. In particular, the workshop aims to link experts from the FDA SEQC2 initiative on Precision Medicine, which will pave the way for defining optimal procedures for the development of actionable drugs that can target phenotype-selected patient groups. We wish to discuss also technical challenges such as working with very large cohorts (e.g. from 60K to 300K to 1M subjects in molecular psychiatry studies) that are now amenable for modeling with deep learning. Further, family cohorts will challenge machine learning and bioinformatics experts for new efficient solutions. In summary, both methodological aspects from deep learning, machine learning, information technology, statistics as well as actual applications, pitfalls and (medical) needs are to be featured.

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.

Recently, a new field has emerged taking benefit of both domains: Data Mining and NLP. The objective of DMNLP is thus to provide a forum to discuss how Data Mining can be interesting for NLP tasks, providing symbolic knowledge, but also how NLP can enhance data mining approaches by providing richer and/or more complex information to mine and by integrating linguistic knowledge directly in the mining process. The workshop aims at bringing together researchers from both communities in order to stimulate discussions about the cross-fertilization of those two research fields. The idea of this workshop is to discuss future directions and new challenges emerging from this cross-fertilization of Data Mining and NLP and in the same time to initiate collaborations between researchers of both communities.

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.

The aim of this workshop is to draw attention to secure multiparty computation (MPC), a subfield of cryptology, as the key foundation for building privacy-preserving data mining (DM) and machine learning (ML). In this approach, sensitive data is typically secret-shared over multiple players, such that those players can jointly perform DM / ML computations, but individual players (or collusions) cannot learn anything about the data, beyond the result of the computations.

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.

This workshop intends to be a top-quality venue to bring together transdisciplinary researchers and practitioners working in topics from multiple areas such as Data Mining, Machine Learning, Numerical Optimization, Public Transport, Traffic Engineering, Multi-Agent Systems, Human-Computer Interaction and Telecommunications, among others. The ultimate goal of this venue is to evaluate not only the theoretical contribution of the data driven methodology proposed in each research work, but also its potential deployment/impact as well as its advances with respect to the State-of-the-Art/State-of-the-Practice in the domains of the related applications.

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.

The challenge of renewable energy integration is inherently multidisciplinary and is particularly dependant on the use of techniques from the domains of data analytics, pattern recognition and machine learning. Examples of relevant research topics include the forecasting of electricity supply and demand, the detection of faults, demand response applications and many others. This workshop will provides a forum where interested researchers from the various related domains will be able to present and discuss their findings.

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 with 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.

Meta-learning leverages knowledge of past algorithm applications to select the best techniques for future applications, and offers effective techniques that are superior to humans both in terms of the end result and especially in the time required to achieve it. In this workshop, we will discuss different ways of exploiting meta-learning techniques to identify the potentially best algorithm(s) for a new task, based on meta-level information, prior experiments on both past datasets and the current one. Many contemporary problems require the use of workflows that consist of several processes or operations. Constructing such complex workflows requires extensive expertise, and could be greatly facilitated by leveraging planning, meta-learning and intelligent system design. This task is inherently interdisciplinary, as it builds on expertise in various areas of AI.

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.

Meta-learning leverages knowledge of past applications of algorithms applications to learn how to select the best techniques for future applications, and offers effective techniques that are superior to humans both in terms of the quality of the end result and even more so in the time required to achieve it. Recent approaches include also (preferably very fast) partial probing runs on a given problem with the aim of determining the best strategy to be used from there onwards. This may include further probing or recommending an algorithm to be used to solve the given problem. A recent trend is to incorporate such techniques into software platforms. This synergy leads to new advances that recommend combinations of algorithms and hyperparameter settings simultaneously, and that speed up algorithm configuration by learning which parameter settings are likely most useful for dealing with the data at hand.

After motivating and introducing the concepts of algorithm selection and configuration, we elucidate how they arise in machine learning and data mining, but also in other domains, such as optimization. We demonstrate how meta-learning techniques can be effectively used in this context, exploiting information gleaned from past experiments as well as by probing the data at hand. Moreover, many current applications require the use of machine learning or data mining workflows that consist of several different processes or operations. Constructing such complex systems or workflows requires extensive expertise, as well as existing meta-data and software, and can be greatly facilitated by leveraging the methodologies presented at this tutorial.

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

Monday, September 18th, 2017

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

Combined Workshops with 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

Friday, September 22nd, 2017

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

Combined Workshops with 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.

Meta-learning leverages knowledge of past algorithm applications to select the best techniques for future applications, and offers effective techniques that are superior to humans both in terms of the end result and especially in the time required to achieve it. In this workshop, we will discuss different ways of exploiting meta-learning techniques to identify the potentially best algorithm(s) for a new task, based on meta-level information, prior experiments on both past datasets and the current one. Many contemporary problems require the use of workflows that consist of several processes or operations. Constructing such complex workflows requires extensive expertise, and could be greatly facilitated by leveraging planning, meta-learning and intelligent system design. This task is inherently interdisciplinary, as it builds on expertise in various areas of AI.

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.

Meta-learning leverages knowledge of past applications of algorithms applications to learn how to select the best techniques for future applications, and offers effective techniques that are superior to humans both in terms of the quality of the end result and even more so in the time required to achieve it. Recent approaches include also (preferably very fast) partial probing runs on a given problem with the aim of determining the best strategy to be used from there onwards. This may include further probing or recommending an algorithm to be used to solve the given problem. A recent trend is to incorporate such techniques into software platforms. This synergy leads to new advances that recommend combinations of algorithms and hyperparameter settings simultaneously, and that speed up algorithm configuration by learning which parameter settings are likely most useful for dealing with the data at hand.

After motivating and introducing the concepts of algorithm selection and configuration, we elucidate how they arise in machine learning and data mining, but also in other domains, such as optimization. We demonstrate how meta-learning techniques can be effectively used in this context, exploiting information gleaned from past experiments as well as by probing the data at hand. Moreover, many current applications require the use of machine learning or data mining workflows that consist of several different processes or operations. Constructing such complex systems or workflows requires extensive expertise, as well as existing meta-data and software, and can be greatly facilitated by leveraging the methodologies presented at this tutorial.

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

TBA

Discovery Challenges

TiSeLaC : Time Series Land Cover Classification Challenge

TBA

Multi-Plant Photovoltaic Energy Forecasting Challenge

TBA

Mars Express Power Challenge

TBA

TBA