[TYPES/announce] CFP: AAAI-2018 Workshop on Declarative Learning Based Programming

[Kordjamshidi, Parisa]

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CALL FOR PARTICIPANTS
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Third International Workshop on Declarative Learning Based Programming (DeLBP-2018), in conjunction with thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), February 3rd, 2018, New Orleans, Louisiana, USA.  Website: http://delbp.github.io<http://delbp.github.io/>.

REGISTRATION DEADLINES

Early:  On or before December 8
Late: On or before January 5
Onsite: After January

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AIM AND SCOPE
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The main goal of Declarative Learning Based Programming (DeLBP) workshop is to investigate the issues that arise when designing and using programming languages that support learning from data and knowledge. DeLBP aims at new programming models and abstractions that facilitate the design and development of intelligent real world applications that use machine learning and reasoning. The challenges of such a programming paradigm include: Interaction with messy, naturally occurring data; Specifying the requirements of the application at a high abstraction level; Dealing with uncertainty in data and knowledge in various layers of the application program; Using representations that support flexible relational feature engineering and learning rich data representations; Using representations that support flexible reasoning and structure learning; Supporting model chaining and composition; Integrating a range of learning and inference algorithms; and finally addressing the above mentioned issues in one unified programming environment. Conventional programming languages offer no help to application programmers that attempt to design and develop applications that make use of real world data, and reason about it in a way that involves learning interdependent concepts from data, incorporating and composing existing models, and reasoning about existing and trained models and their parameterization. Over the last few years, the research community has tried to address these problems from multiple perspectives, most notably various approaches based on Probabilistic programming, Logical Programming and the integrated paradigms. The goal of this workshop is to present and discuss the current related research and the way various challenges have been addressed. We aim at motivating the need for further research toward a unified framework in this area based on the key existing paradigms: Probabilistic Programming, Logic Programming, Probabilistic Logical Programming, First-order query languages and database management systems and deductive databases, Statistical relational learning, Deep Learning and related languages, and connect these to the ideas of Learning Based Programming. We aim to discuss and investigate the required type of languages and representations that facilitate modeling complex learning models, deep architectures, and provide the ability to combine, chain and perform flexible inference with existing models and by exploiting domain knowledge.
Though the theme of this workshop remains generic as in the past versions, we will aim at emphasizing on ideas and opinions regarding conceptual representations of deep learning architectures that connect various computational units to the semantics of declarative data and knowledge representations.
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TOPICS OF INTEREST
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—New abstractions and modularity levels towards a unified framework for (deep/structured) learning and reasoning,
  ◦ Frameworks/Computational models to combine learning and reasoning paradigms and exploit accomplishments in AI from various perspectives.
—Flexible use of structured and relational data from heterogeneous resources in learning.
    ◦ Data modeling (relational/graph-based databases) issues in such a new integrated framework for learning based on data and knowledge.
—Exploiting knowledge such as expert knowledge and common sense knowledge expressed via multiple formalisms, in learning.
—The ability of closing the loop to acquire knowledge from data and data from knowledge towards life-long learning, and reasoning.
—Using declarative domain knowledge to guide the design of learning models,
    ◦ Including feature extraction, model selection, dependency structure and deep learning architecture.
—Automation of hyper-parameter tuning.
—Design and representation of complex learning and inference models.
—The interface and software tools for learning-based programming,
    ◦ Either in the form of programming languages, declarations, frameworks, libraries or graphical user interfaces.
—Storage and retrieval of trained learning models in a flexible way to facilitate incremental learning.
—Related applications in Natural language processing, Computer vision, Bioinformatics, Computational biology, multi-agent systems, etc.
—Learning to learn programs.
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KEYNOTE SPEAKERS
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*Sebastian Riedel, University College London

*William Cohen, Carnegie Mellon University

*Avi Pfeffer, Charles River Analytics
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PROGRAM COMMITTEE
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 Guy Van den Broeck<http://people.cs.kuleuven.be/%7Eguy.vandenbroeck/>, University of California, Los Angeles
 Sameer Singh<http://sameersingh.org/>, University of California, Irvine
 Rodrigo de Salvo Braz<http://www.ai.sri.com/%7Ebraz/>, SRI International
 Christos Christodoulopoulos<http://christos-c.com/>, Amazon Cambridge, UK
 William Wang<https://www.cs.ucsb.edu/~william/>, University of California, Santa Barbara
 Kai-Wei Chang<http://www.cs.virginia.edu/~kc2wc/>, University of Virginia
 Nikolaos Vasiloglou, Ismion Inc
 Martin Mladenov, Technical University of Dortmund
 Tias Guns, Vrije University of Brussels
 Umar Manzoor, Tulane University
Mark Kaminski, University of Oxford
Avi Pfeffer, Charles River Analytics
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ORGANIZING COMMITTEE
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Parisa Kordjamshidi<http://people.cs.kuleuven.be/%7Eparisa.kordjamshidi/>,  Tulane University, IHMC
Dan Roth<http://l2r.cs.uiuc.edu/>,  University of Pennsylvania
Dan Goldwasser<https://www.cs.purdue.edu/homes/dgoldwas/>,  Purdue University
Kristian Kersting<http://www-ai.cs.uni-dortmund.de/PERSONAL/kersting.html>,  TU Darmstadt
Nikolaos Vasiloglou, Ismion Inc
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CONTACT:  delbp-3 at googlegroups.com<mailto:delbp-3 at googlegroups.com> (Organization Committee)
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Kordjamshidi, Parisa
Assistant Professor
CS Department at Tulane University
Research Scientist at IHMC<https://www.ihmc.us>
Homepage<http://www.cs.tulane.edu/~pkordjam/>



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