October 5 – 10 , 2014, Dagstuhl Seminar 14411
Constraints, Optimization and Data
Siegfried Nijssen (KU Leuven, BE)
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Constraint programming and optimization (CPO) have recently received considerable attention from the fields of machine learning and data mining (MLDM). On the one hand, the hypotheses and patterns that one seeks to discover in MLDM can be specified in terms of constraints (e.g. labels in the case of supervised learning, preferences in the case of learning to rank, must-link and cannot-link in the case of unsupervised learning, coverage and lift in the case of data mining). On the other hand, powerful constraint programming solvers have been developed. If MLDM users express their requirements in terms of constraints they can delegate the MLDM process to such highly efficient solvers.
Conversely, CPO can benefit from integrating learning and mining functionalities in a number of ways. For example, formulating a real-world problem in terms of constraints requires significant expertise in the problem domain. Also, selecting the most appropriate constraints, in terms of constraint solving efficiency, requires considerable expertise in the CPO domain. In other words, experience plays a major role in successfully applying CPO technology.
In addition, both CPO and MLDM share a common challenge associated with tuning their respective methods, specifically determining the best parameters to chose for an algorithm depending on the task at hand. A typical performance metric in machine learning is the predictive accuracy of a hypotheses, while in CPO it might be search cost or solution quality.
This seminar built upon the 2011 Constraint Programming meets Machine Learning and Data Mining and the 2014 Preference learning seminars. Its goal was to identify the key challenges and opportunities at the crossroads of CPO and MLDM. The interests of the participants included the following:
- Problem formulation and modelling: constraint-based modelling; preference formalisms; loss functions in ML; modelling and exploiting background knowledge; structured properties (e.g. preserving spatio-temporal structures).
- Improvement of algorithms / platforms in the areas of algorithm selection, algorithm configuration, and/or algorithm scheduling, particularly with respect to parallel execution.
- Specification and reasoning about goals and optimization criteria: modelling preferences and integrating with human expertise (exploiting the "human in the loop") to converge on high quality outcomes.
- Additional functionalities such as the use of visualization and explanation.
- Algorithmic scalability.
- Approximate reasoning, reasoning under uncertainty, and incorporating probability.
The seminar was organized into seven sessions: frameworks and languages; algorithm configuration; constraints in pattern mining; learning constraints; machine learning with constraints; applications; and demonstrations. The demonstrations presented at the seminar were by:
- Guido Tack - MiniZinc (see http://mininzinc.org);
- Joaquin Vanschoren - OpenML (see http://openml.org);
- Tias Guns - MiningZinc (see http://dtai.cs.kuleuven.be/CP4IM/miningzinc);
- Bruno Crémilleux - software for the calculation of Sky Pattern Cubes;
- Marc Denecker - IDP (see http://dtai.cs.kuleuven.be/krr/software/idp);
- Holger Hoos - algorithm selection and portfolio software;
- Luc De Raedt - ProbLog (see http://dtai.cs.kuleuven.be/problog/).
The seminar also had five working groups on:
- Declarative Languages for Machine Learning and Data Mining;
- Learning and Optimization with the Human in the Loop;
- Meta-Algorithmic Techniques;
- Big Data;
- Towards Killer Applications.
Creative Commons BY 3.0 Unported license
Luc De Raedt and Siegfried Nijssen and Barry O'Sullivan and Michele Sebag
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