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 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 domain 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 new innovative abstractions and 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. Moreover, in the recent years several Deep Learning tools have created easy to use abstractions for programming model configurations for deep architectures. 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 and deep architectures 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.
HighlightThough 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. We also encourage submissions on learning to learn programs.
Topics SummaryThe main research questions and topics of interest include the following existing topics in the context of an integrated learning based paradigm:
- 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 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 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, multi-agent systems, etc.
- Learning to learn programs
- Sebastian Riedel, University College London
- William Cohen, Carnegie Mellon University More to be announced ...
- Golnoosh Farnadi, Behrouz Babaki and Lise Getoor, Fairness-aware Relational Learning and Inference.
- Michelangelo Diligenti, Soumali Roychowdhury and Marco Gori, Image Classification Using Deep Learning and Prior Knowledge.
SubmissionsWe encourage contributions with either a technical paper (AAAI style, 6 pages without references), a position statement (AAAI style, 2 pages maximum) or an abstract of a published work. AAAI Style files available here. Please make submissions via EasyChair, here.
- Submission Deadline:
October 20th, 2017Extended to October 31st
- Notification: November 13th, 2017
- Workshop Days: February 3, 2018
||Tulane University, IHMCemail@example.com|
|University of Pennsylvaniafirstname.lastname@example.org|
||University of California, Los Angeles|
|University of California, Irvine|
|Vrije University of Brussels|
||Amazon Cambridge, UK|
|University of California, Santa Barbara|
|Technical University of Dortmund|
|University of California, Los Angeles|
|University of Oxford|
|Charles River Analytics|