[TYPES/announce] [CFP]: IJCAI-2019 Workshop on "Declarative Learning Based Programming"

[Kordjamshidi, Parisa]

CALL FOR PAPERS and PARTICIPANTS
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Forth International Workshop on Declarative Learning Based Programming (DeLBP-2019), in conjunction with 28th International Joint Conference on Artificial Intelligence (IJCAI-2019), August 10-16, 2019, Macao, China. Website: http://delbp.github.io<http://delbp.github.io/>.

Submission Deadline: April 30th, 2019

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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 relational learning with 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. 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, End-to-End differentiable programming 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.

Highlight: Though the theme of this workshop remains generic as in the past versions, we will aim at emphasizing on ideas and opinions regarding using different types of knowledge (Declarative, procedural) in Statistical/Neural learning.

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TOPICS OF INTEREST
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  *   New programming abstractions and modularity levels towards a unified framework for (deep/structured) learning and reasoning,
     *   Frameworks/Computational models to combine learning and reasoning paradigms.
  *   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.
  *   The ability of closing the loop to acquire knowledge from data and data from knowledge towards life-long learning, and reasoning.
  *   Exploiting declarative and procedural knowledge such as expert knowledge and common sense knowledge expressed via multiple formalisms, in learning.
  *   Using declarative domain knowledge to guide the design of learning models,
     *   including feature extraction, model selection, dependency structure and deep model architecture.
  *   Design and representation of complex learning and inference models.
  *   The interface for learning-based programming,
     *   either in the form of programming languages, declarations, frameworks, libraries or graphical user interfaces.
  *   Related applications in Natural language processing, Computer vision, Bioinformatics, Computational biology, multi-agent systems, etc.
  *   End-To-End differential programming, Learning to learn programs and program synthesis if considering our specific perspective related to learning-based programs.

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INVITED SPEAKERS
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1) Guy Van den Broeck , University of California Los Angeles

2) ..TBD

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IMPORTANT DATES
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Submission Deadline: April 30th, 2019
Notification: May 20th, 2019
Workshop Days: August 10-12, 2019
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SUBMISSION INFORMATION
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We encourage contributions with either a technical paper (IJCAI style, 6 pages without references), a position statement (IJCAI style, 2 pages maximum) or an abstract of a published work. IJCAI Style files available here. Please make submissions via EasyChair, here.
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ORGANIZING COMMITTEE
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Parisa Kordjamshidi,  Tulane University, IHMC
Hannaneh Hajishirazi, University of Washington
Quan Guo , Tulane University
Nikolaos Vasiloglou, Ismion Inc
Kristian Kersting,  TU Darmstadt
Dan Roth,  University of Pennsylvania
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CONTACT
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delbp-4 at googlegroups.com<mailto:delbp-4 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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