International Workshop and Tutorial on

Interactive Adaptive Learning (IAL2017)

Co-Located With The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2017)

18 September 2017 - Skopje (Macedonia)


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Science, technology, and commerce increasingly recognise the importance of machine learning approaches for data-intensive, evidence-based decision making.

This is accompanied by increasing numbers of machine learning applications and volumes of data. Nevertheless, the capacities of processing systems or human supervisors or domain experts remain limited in real-world applications. Furthermore, many applications require fast reaction to new situations, which means that first predictive models need to be available even if little data is yet available. Therefore approaches are needed that optimise the whole learning process, including the interaction with human supervisors, processing systems, and data of various kind and at different timings: techniques for estimating the impact of additional resources (e.g. data) on the learning progress; techniques for the active selection of the information processed or queried; techniques for reusing knowledge across time, domains, or tasks, by identifying similarities and adaptation to changes between them; techniques for making use of different types of information, such as labelled or unlabelled data, constraints or domain knowledge. Such techniques are studied for example in the fields of adaptive, active, semi-supervised, and transfer learning. However, this is mostly done in separate lines of research, while combinations thereof in interactive and adaptive machine learning systems that are capable of operating under various constraints, and thereby address the immanent real-world challenges of volume, velocity and variability of data and data mining systems, are rarely reported. Therefore, this combined tutorial and workshop aims to bring together researchers and practitioners from these different areas, and to stimulate research in interactive and adaptive machine learning systems as a whole.

This 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. 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.

In particular, we welcome contributions that address aspects including, but not limited to:

    Novel Techniques for Active, Semi-Supervised, Transfer Learning
  • methods for big, evolving, or streaming data,
  • methods for recent complex model structures such as deep learning neural networks or recurrent neural networks,
  • methods for interacting with imperfect or multiple oracles, e.g. learning from crowds,
  • methods for incorporating domain knowledge and constraints,
  • methods for timing the interaction and for combining different types of information,
  • online and ensemble methods for evolving models and systems, with specific switching and fusion techniques,
  • Innovative Use and Applications of Active, Semi-Supervised, Transfer Learning
  • for filtering, forgetting, resampling,
  • for active class or feature selection, e.g. from multi-modal data,
  • for detection of change, outliers, frauds, or attacks,
  • new interactive learning protocols and application scenarios, e.g., brain-computer interfaces, crowdsourcing, ...
  • in application in data-intensive science,
  • in applications with real-world deployment,
  • Techniques for Combined Interactive Adaptive Learning
  • methods combining adaptive, active, semi-supervised, or transfer learning techniques,
  • cost-aware methods and methods for estimating the impact of employing additional resources, such as data or processing capacities, on the learning progress,
  • methodologies for the evaluation of such techniques, and comparative studies,
  • methods for automating the control of an interactive adaptive learning process.

Important dates

The following timeline shows the most important dates for the workshop.

  • Submission open

    15 Mai 2017

    You can submit your contributions via EasyChair.

  • Submission deadline EXTENDED

    12 July 2017 (notification before 28 July, early bird rate)
    28 July 2017

  • Notification

    28 July 2017 (papers submitted until 12 July)
    16 August 2017 (papers submitted after 12 July)

  • Camera Ready

    23 August 2017

  • Workshop + Tutorial (One Day)

    18 September 2017

    Co-Located With The The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2017).

Submit your contribution

You can submit your contribution via EasyChair. Please use the link below.

Open Access

All accepted papers will be published at (indexed by e.g. google scholar). You are allowed to share, upload and distribute your paper.


The paper must be be written in English and contain author names, affiliations, and email addresses. The paper must be in PDF.

Regular papers (max. 10 pages)

The page limit for contributions is 10 pages (excluding references and supplemental material) in the LNCS format. See instructions here.

Single-blind reviews

We use a single-blinded review process, papers need not to be anonymized.


All accepted paper are presented in spotlight talks and/or poster sessions. At least one author of each accepted paper must be registered to the workshop.

Short papers (max. 2 pages)

We also welcome works-in-progress or industrial experiences in LNCS format. See instructions here.


Time Program Presenter/Author
09:00 - 09:10 Welcome Organizing Committee
09:10 - 09:50 Tutorial Part 1
Introduction to Stream Mining
Georg Krempl
09:50 - 10:40 Tutorial Part 2
Active Learning
Daniel Kottke
Morning Coffee Break
11:00 - 11:40 Tutorial Part 3
Semi-Supervised and Transfer Learning
Georg Krempl
11:40 - 12:30 Tutorial Part 4
Evaluation, Applications and Emerging Trends
Vincent Lemaire
  Spotlights on Poster Session  
12:30 -   Short Paper 1
Probabilistic Expert Knowledge Elicitation of Feature Relevances in Sparse Linear Regression
Pedram Daee, Tomi Peltola, Marta Soare, and Samuel Kaski
  Short Paper 2
Users behavioural inference with Markovian decision process and active learning
Firas Jarboui, Vincent Rocchisani, and Wilfried Kirchenmann
  - 12:40 Short Paper 3
Multi-Arm Active Transfer Learning for Telugu Sentiment Analysis
Subba Reddy Oota, Vijayasaradhi Indurthi, Mounika Reddy Marreddy, Sandeep Sricharan Mukku, and Radhika Mamidi
Lunch Break + Poster Session
14:00 - 14:20 Talk 1
Probabilistic Active Learning with Structure-Sensitive Kernels
Dominik Lang, Daniel Kottke, Georg Krempl, and Bernhard Sick
14:20 - 14:40 Talk 2
Transfer learning for time series anomaly detection
Vincent Vercruyssen, Wannes Meert, and Jesse Davis
14:40 - 15:40 Invited Talk
Ensemble learning from data streams with active and semi-supervised approaches
Bartosz Krawczyk
Afternoon Coffee Break + Poster Session
16:00 - 16:20 Talk 3
Simulation of Annotators for Active Learning: Uncertain Oracles
Adrian Calma and Bernhard Sick
16:20 - 16:40 Talk 4
Interactive Anonymization for Privacy aware Machine Learning
Bernd Malle, Peter Kieseberg, and Andreas Holzinger
16:40 - 17:40 Panel Discussion George Kachergis, Bartosz Krawczyk, Myra Spiliopoulou, and Jerzy Stefanowski

Tutorial contents

Part 1 - Introduction to Stream Mining

This part starts with the classic stream mining paradigm. In its context, we discuss the challenges posed by non-stationarity and limitations in processing, storage, and supervision capacities. We briefly summarize related techniques, e.g. for incremental processing, forgetting, and change detection. This part concludes by an overview on further challenges that are investigated in the state-of-the-art research.

Part 2 - Active Learning

In this part of the tutorial, we focus on techniques for optimising the interaction of a machine learning system with an oracle such as a human supervisor. We review active machine learning techniques, with focus on adaptive active learning for evolving and streaming data. We discuss recent advances and conclude with an overview on open research questions in adaptive active machine learning.

Part 3 - Semi-Supervised and Transfer Learning

This part of the tutorial addresses the problem of learning with incomplete or delayed supervision. We focus on the problem of learning with verification latency, and review techniques from change mining, semi-supervised and (unsupervised) transfer learning in non-stationary environments. We conclude with an overview on open challenges.

Part 4 - Evaluation, Applications and Emerging Trends

This last part of the tutorial takes an integrative view on the previous parts, with focus on industrial applications and open challenges of adaptive interactive mining systems as a whole. We briefly discuss the related issues of evaluation and deployment, applications, reported challenges and solutions, and highlight potential directions for future research.

Invited Talk by Bartosz Krawczyk

Ensemble learning from data streams with active and semi-supervised approaches

Developing efficient classifiers which are able to cope with big and streaming data, especially with the presence of the so-called concept drift is currently one of the primary directions among the machine learning community. This presentation will be devoted to the importance of ensemble learning methods for handling drifting and online data. It has been shown that a collective decision can increase classification accuracy due to mutually complementary competencies of each base learner. This premise is true if the set consists of diverse and mutually complementary classifiers. For non-stationary environments, diversity may also be viewed as a changing context — which makes them an excellent tool for handling data shifts. The main focus of the lecture will be given to using these mentioned advantages of ensemble learning for data stream mining on a budget. As streaming data is characterized by both massive volume and velocity one cannot assume unlimited access to class labels. Instead methods that allow to reduce the number of label queries should be sought after. Recent trends in combining active and semi-supervised learning with ensemble solutions, such as online Query by Committee or Self-Labeling Committees, will be presented. Additionally, this talk will offer discussion on emerging challenges and future directions in this area.

Bartosz Krawczyk is an assistant professor in the Department of Computer Science, Virginia Commonwealth University, Richmond VA, USA, where he heads the Machine Learning and Stream Mining Lab. (


Organizing Committee (ial2017 (at)

Georg Krempl

georg.krempl (at)
University Magdeburg, Germany

Vincent Lemaire

vincent.lemaire (at)
Orange Labs, France

Robi Polikar

polikar (at)
Rowan University, USA

Bernhard Sick

bsick (at)
University of Kassel, Germany

Daniel Kottke

daniel.kottke (at)
University of Kassel, Germany

Adrian Calma

acalma (at)
University of Kassel, Germany

Program Committee (to be completed):

Michael Beigl (KIT)
Giacomo Boracchi (Politecnico di Milano)
Bartosz Krawczyk (Virginia Commonwealth University)
Mark Embrechts (Rensselaer Polytechnic Institute)
Michael Granitzer (University Passau)
Barbara Hammer (University Bielefeld)
Henner Heck (University Kassel)
Vera Hofer (University Graz)
George Kachergis (Radboud University)
Christian Müller-Schloer (University Hannover)
Christin Seifert (TU Dresden)
Ammar Shaker (University Paderborn)
Jasmina Smailovic (Josef Stefan Institute)
Myra Spiliopoulou (University Magdeburg)
Jurek Stefanowski (University Poznan)
Dirk Tasche (Swiss Financial Market Supervisory Authority FINMA)
Martin Znidarsic (Jožef Stefan Institute)