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Synth SIGNED

Synthesising Inductive Data Models

Total Cost €

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EC-Contrib. €

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Partnership

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Project "Synth" data sheet

The following table provides information about the project.

Coordinator
KATHOLIEKE UNIVERSITEIT LEUVEN 

Organization address
address: OUDE MARKT 13
city: LEUVEN
postcode: 3000
website: www.kuleuven.be

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Belgium [BE]
 Project website https://synth.cs.kuleuven.be/
 Total cost 2˙458˙656 €
 EC max contribution 2˙458˙656 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2015-AdG
 Funding Scheme ERC-ADG
 Starting year 2016
 Duration (year-month-day) from 2016-09-01   to  2021-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    KATHOLIEKE UNIVERSITEIT LEUVEN BE (LEUVEN) coordinator 2˙458˙656.00

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 Project objective

Inspired by recent successes towards automating highly complex jobs like programming and scientific experimentation, the ultimate goal of this project is to automate the task of the data scientist when developing intelligent systems, which is to extract knowledge from data in the form of models. More specifically, this project wants to develop the foundations of a theory and methodology for automatically synthesising inductive data models. An inductive data model (IDM) consists of 1) a data model (DM) that specifies an adequate data structure for the dataset (just like a database), and 2) a set of inductive models (IMs), that is, a set of patterns and models that have been discovered in the data. While the DM can be used to retrieve information about the dataset and to answer questions about specific data points, the IMs can be used to make predictions, propose values for missing data, find inconsistencies and redundancies, etc. The task addressed in this project is to automatically synthesise such IMs from past data and to use these to support the user when making decisions. It will be assumed that the data set consists of a set of tables, that the end-user interacts with the IDM via a visual interface, and the data scientist via a unifying IDM language offering a number of core IMs and learning algorithms. The key challenges to be tackled in SYNTH are: 1) the synthesis system must ”learn the learning task”, that is, it should identify the right learning tasks and learn appropriate IMs for each of these; 2) the system may need to restructure the data set before IM synthesis can start; and 3) a unifying IDM language for a set of core patterns and models must be developed. The approach will be implemented in open source software and evaluated on two challenging application areas: rostering and sports analytics.

 Publications

year authors and title journal last update
List of publications.
2019 Pedro Miguel Zuidberg Dos Martires, Samuel Kolb, Luc De Raedt
How to Exploit Structure while Solving Weighted Model Integration Problems
published pages: , ISSN: , DOI:
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence 2019-11-08
2019 Stefano Teso, Luca Masera, Michelangelo Diligenti, Andrea Passerini
Combining learning and constraints for genome-wide protein annotation
published pages: , ISSN: 1471-2105, DOI: 10.1186/s12859-019-2875-5
BMC Bioinformatics 20/1 2019-11-08
2019 Samuel Kolb, Stefano Teso, Anton Dries, Luc De Raedt
Predictive spreadsheet autocompletion with constraints
published pages: , ISSN: 0885-6125, DOI: 10.1007/s10994-019-05841-y
Machine Learning 2019-11-08
2019 Arcchit Jain; Tal Friedman; Ondrej Kuzelka; Guy Van den Broeck; Luc De Raedt
Scalable Rule Learning in Probabilistic Knowledge Bases
published pages: , ISSN: , DOI:
Automated Knowledge Base Construction 2019-10-30
2018 Paolo Dragone, Stefano Teso, Andrea Passerini
Constructive Preference Elicitation over Hybrid Combinatorial Spaces
published pages: , ISSN: , DOI:
Proceedings Thirty-Second AAAI Conference on Artificial Intelligence 2019-06-13
2017 Hendrik Blockeel
Declarative data analysis
published pages: 217-223, ISSN: 2364-415X, DOI: 10.1007/s41060-017-0081-y
International Journal of Data Science and Analytics volume 6/3 2019-06-13
2017 Samuel Kolb, Sergey Paramonov, Tias Guns, Luc De Raedt
Learning constraints in spreadsheets and tabular data
published pages: 1441-1468, ISSN: 0885-6125, DOI: 10.1007/s10994-017-5640-x
Machine Learning 106/9-10 2019-06-13
2018 Paolo Dragone, Stefano Teso, Mohit Kumar, Andrea Passerini
Decomposition strategies for constructive preference elicitation
published pages: , ISSN: , DOI:
Proceedings Thirty-Second AAAI Conference on Artificial Intelligence 2019-06-13
2018 Luc De Raedt, Andrea Passerini, Stefano Teso
Learning constraints from examples
published pages: , ISSN: , DOI:
Proceedings Thirty-Second AAAI Conference on Artificial Intelligence 2019-06-13
2018 Elia Van Wolputte, Evgeniya Korneva, Hendrik Blockeel
MERCS: Multi-directional Ensembles of Regression and Classification Trees
published pages: , ISSN: , DOI:
Proceedings Thirty-Second AAAI Conference on Artificial Intelligence 2019-06-13
2017 Stefano Teso, Andrea Passerini, Paolo Viappiani
Constructive Preference Elicitation for Multiple Users with Setwise Max-margin
published pages: 3-17, ISSN: , DOI:
 Algorithmic Decision Theory. ADT 2017. Lecture Notes in Computer Science, vol 10576. 2019-06-13
2018 Paolo Dragone, Stefano Teso, Andrea Passerini
Constructive Preference Elicitation
published pages: , ISSN: 2296-9144, DOI: 10.3389/frobt.2017.00071
Frontiers in Robotics and AI 4 2019-06-13
2017 Sergey Paramonov, Tao Chen, Tias Guns
Generic mining of condensed pattern representations under constraints
published pages: 168-177, ISSN: , DOI:
YSIP2 – Proceedings of the Second Young Scientist\'s International Workshop on Trends in Information Processing Vol. 1837 2019-05-27
2019 Stefano Teso, Kristian Kersting
Explanatory Interactive Machine Learning
published pages: , ISSN: , DOI:
Proceedings of AAAI/ACM Conference on Artificial Intelligence, Ethics and Society 2019 2019-05-27
2019 Tijl De Bie; Luc De Raedt, Holger H. Hoos, Padhraic Smyth
Automating Data Science (Dagstuhl Seminar 18401)
published pages: , ISSN: , DOI:
Dagstuhl Reports Volume 8, Issue 9 2019-05-27
2019 Pedro Miguel Zuidberg Dos Martires, Anton Dries, Luc De Raedt
Exact and Approximate Weighted Model Integration with Probability Density Functions Using Knowledge
published pages: , ISSN: , DOI:
Proceedings of the 30th AAAI Conference on Artificial Intelligence 2019-05-27
2018 Mohit Kumar, Stefano Teso, Luc De Raedt
Automating Personnel Rostering by Learning Constraints Using Tensors
published pages: , ISSN: , DOI:
arXiv:1805.11375 2019-05-27

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The information about "SYNTH" are provided by the European Opendata Portal: CORDIS opendata.

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