Demand for real world applications
Nowadays, to solve real-world problems in many areas such as cognitive sciences, biology, finance, physics, social sciences, etc, scientists think about data-driven solutions to a progressively increasing extent.
Challenge for domain expertsHowever, current technologies offer cumbersome solutions along multiple dimensions. Some of this include, Interaction with messy naturally occurring data; Necessity for expertise in CS and extensive programming; Necessity of exploiting various learning paradigms and techniques; and Extensive experimental exploration for model selection, feature selection, parameter tuning due to the lack of theoretical evidence about the effectiveness of various models.
High-level goal and implied directions
DeLBP workshop aims at highlighting the issues and challenges that arise for having a declarative data driven problem-solving paradigm. This paradigm facilitates and simplifies the design and the development of intelligent real world applications that consider learning from data and reasoning based on knowledge. It highlights the challenges in making machine learning accessible to various users including non-CS-experts and application programmers.
Conventional programming languages have not been primarily designed to offer help for the above-mentioned challenges. To Achieve the DeLBP goals there is a need to go beyond designing tools for classic machine learning by enriching the existing solutions and frameworks with the capabilities in:
Specifying the requirements of the application at a high abstraction level; Exploiting the expert knowledge in learning; 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; Ability to reuse, combining and chaining models and perform flexible inference on complex models or pipelines of decision making; Integrating a range of learning and inference algorithms; Closing the loop of moving from data to knowledge and exploiting knowledge to generate data; and finally having a unified programming environment to design application programs.
Related communitiesOver the last few years the research community has tried to address these problems from multiple perspectives, most notably various approaches based on Probabilistic programming (PP), Logical Programming (LP), Constrained Conditional models (CCM) and other integrated paradigms such as Probabilistic Logical Programming (PLP) and Statistical relational learning (SRL). These paradigms and related languages aim at learning over probabilistic structures and exploiting knowledge in learning. We aim at motivating the need for further research toward a unified framework in this area based on the above mentioned key existing paradigms as well as other related research such as First-order query languages, database management systems (DBMS), deductive databases (DDB), hybrid optimization for learning from data and knowledge. We are interested in connecting these ideas towards developing a Declarative Learning Based Programming Paradigm and investigate the required type of languages, representations and computational models to support such a paradigm.
Topics SummaryThe main research questions and topics of interest include the following existing topics in the context of an integrated learning based paradigm:
- 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 model architecture.
- 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.
ScheduleTo come ...
- Dan Roth, University of Illinois at Urbana-Champaign. Tentative
- Nikolaos Vasiloglou, LogicBlox. Confirmed
SubmissionsWe 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.
- Submission Deadline: May 8th, 2017
- Notification: June 5th, 2017
- Workshop Days: August 19th-20th, 2017
||Tulane University, IHMCemail@example.com|
|University of Illinois at Urbana-Champaignfirstname.lastname@example.org|
|University of Texas at Dallasemail@example.com|
|Technical University of Dortmundfirstname.lastname@example.org|
||University of California, Los Angeles|
|University of California, Irvine|
|Charles River Analytics|
|Vrije University of Brussels|
||Amazon Cambridge, UK|
|University of California, Santa Barbara|
|Technical University of Dortmund|
|University of Virginia|
|University College London|