DeLBP 2017 - 2nd International Workshop on Declarative Learning Based Programming (DeLBP 2017)

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 facilitating and simplifying the design and development of intelligent real world applications that use machine learning and reasoning by addressing the following commonly observed challenges: 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; Using representations that support flexible reasoning and structure learning; 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 parametrization. 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 (PP), Logic Programming (LP), Probabilistic Logical Programming (PLP), First-order query languages and database management systems (DBMS) and deductive databases (DDB), Statistical relational learning and related languages (SRL), 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 probabilistic or non-probabilistic complex learning models, deep architectures, and provide the ability to combine, chain and perform flexible inference with existing models and by exploiting domain knowledge. (Website: http://delbp.github.io)
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TOPICS OF INTEREST
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— New abstractions and modularity levels towards a unified framework for 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 ) 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.
— Structure Learning and automation of hyper-parameter tuning.
— 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.
— 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, etc.
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IMPORTANT DATES
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• Submission Deadline: May 8th, 2017 => May 20th, 2017
• Notification: June 5th, 2017
• Workshop Days: August 19th-20th, 2017
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SUBMISSION AND SELECTION PROCESS
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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 [http://ijcai-17.org/FormattingGuidelinesIJCAI-17.z...]. Please make submissions via EasyChair, here [https://easychair.org/conferences/?conf=delbp2017].
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PROGRAM COMMITTEE
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Guy Van den Broeck, University of California, Los Angeles
Sameer Singh, University of California, Irvine
Avi Pfeffer, Charles River Analytics
Rodrigo de Salvo Braz, SRI International
Tias Guns, Vrije University of Brussels
Christos Christodoulopoulos, Amazon Cambridge, UK
William Wang, University of California, Santa Barbara
Kai-Wei Chang, University of Virginia
Martin Mladenov, Technical University of Dortmund
Sebastian Riedel, University College London
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ORGANIZING COMMITTEE
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Parisa Kordjamshidi, Tulane University, IHMC
Dan Roth, University of Illinois at Urbana-Champaign
Jan-Willem Van den Meent, Northeastern University
Dan Goldwasser, Purdue University
Vibhav Gogate, University of Texas at Dallas
Kristian Kersting, TU Dortmund University
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CONTACT
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delbp-2-AT-googlegroups.com (Organization Committee)
pkordjam-AT-tulane.edu
Please circulate this CFP among your colleagues and students.