posted by organizer: jorallo || 2831 views || tracked by 3 users: [display]



When Sep 20, 2019 - Sep 20, 2019
Where Wurzburg, Germany
Submission Deadline Jun 14, 2019
Notification Due Jul 19, 2019
Final Version Due Jul 26, 2019
Categories    data science   machine learning   automation   artificial intelligence

Call For Papers

Wurzburg, Germany, Friday 20 September 2019



Submission deadline: Friday, June 14, 2019
Acceptance notification : Friday, July 19, 2019
Camera-ready deadline: Monday, July 26, 2019


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).


This ECMLPKDD workshop wants to bring together researchers from all areas concerned with data science in order to study whether, to what extent, and how data science can be automated. It will focus on the following Data Science topics:

- Data Wrangling
- Predictive Modeling
- Exploratory Data Analysis
- Inductive querying
- Probabilistic Programming
- Visual Analytics

and will aim at answering the following questions:

1. How can we automatically tune the parameters or configure algorithms? How can we apply this to machine learning and data science algorithms? This is related to expert / rule-based systems, information criteria, statistical learning theory, learning to learn, meta-learning, etc.
2. How can we assist users in their exploratory data mining tasks? Can we automate it? What type of interactivity is needed? How to obtain models of the user and of interestingness?
3. How can we support the data-wrangling process? How can inductive programming techniques help? Can it be realized fully automatically? What are the limitations and opportunities?
4. How can we automate data-driven story-telling? How can we explain learned models to the user? To what extent can natural language be used?
5. Can we (partially) automate Visual Analytics? Can we automatically visualize what is of interest to the user?
6. What is the trade-off between automation and interaction? To what extent is automation (un)desirable?
7. How can probabilistic programming and inductive querying techniques be used to facilitate data science ?
8. How can automation be married with the increasing tendency for personalization? With the impact on privacy and society of data science, are there any additional ethical issues to be taken into account?
9. How can the logs and other recorded information from the work of data scientists be used for assisting and automating part of their workflow?
10. Data Science for the expert versus for the layperson: different optimal trade-offs?


We welcome submissions to the workshop of the following types:
1. Presentations of relevant work that has recently been published or has already been accepted for publication in journals such as DMKD, MLJ, JMLR, AIJ, JAIR, and major conferences such as SIGKDD, ICML, IJCAI, etc. The submission should in this case only consist of a copy of the other paper.
2. Long papers reporting on new material. Papers can be at most 16 pages in the Springer LNCS format. Please note that also shorter papers are welcome.
3. Extended abstracts that report on novel and preliminary ideas. Extended abstracts can be at most 6 pages in LNCS format.
4. Short position statements on automating data science, at most 6 pages in LNCS format.

Review process and LNCS proceedings

The program committee will review all submissions. It will also decide which accepted submissions can be presented orally, in spotlights and/or as posters. Authors of original accepted submissions (i.e. of types 2, 3, and 4) will be given the option of including their submission in an LNCS proceedings volume. Authors who prefer their contribution not to be formally published, so as not to preclude publication elsewhere, can opt-out of this possibility.

Note that for each accepted contribution at least one author must register for the conference.

Workshop chairs:

Tijl De Bie (UGent, Belgium)
Luc De Raedt (KU Leuven, Belgium)
Jose Hernandez-Orallo (Universitat Politecnica de Valencia, Spain)

Programme Committee:

Pavel Brazdil
Jesse Davis
Peter Flach
Cesar Ferri
Elisa Fromont
Holger Hoos
Ernesto Jimenez-Ruiz
Jefrey Lijffijt
Alfredo Nazabal
Siegfried Nijssen
Jose Oramas M.
Andrea Passerini
Maria Perez Ortiz
Bernhard Pfahringer
Padhraic Smyth
Alexandre Termier
Heike Trautmann
Gertjan van den Burg
Matthijs van Leeuwen
Joaquin Vanschoren
Chris Williams

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AIKE 2020   IEEE Artificial Intelligence & Knowledge Engineering 2020