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 experts

However, 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 communities

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 (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 Summary

The main research questions and topics of interest include the following existing topics in the context of an integrated learning based paradigm:


8:45-9:15 Workshop Overview. DeLBP aims and challenges Parisa Kordjamshidi
9:15-10:00 Invited Talk. Declarative Data Science [Slides] Nikolaos Vasiloglou
10:00-10:30 Coffee Break
10:30-11:15 Invited Talk. Declarative constraint-based pattern mining: from modeling to solving [Slides] Tias Guns
11:15-11:35 Invited Paper. Foundations of Declarative Data Analysis Using Limit Datalog Programs [Slides] Mark Kaminski, Bernardo Cuenca Grau, Egor Kostylev, Boris Motik, Ian Horrocks
11:35-11:55 Accepted Paper. Data Science with Linear Programming [Slides] Nantia Makrynioti, Nikolaos Vasiloglou, Emir Pasalic and Vasilis Vassalos
11:55-12:15 Accepted Paper. Scaffolding the Generation of Machine Learning Models with SciRise [Slides] Aneesha Bakharia
12:15-2:15 Lunch Break
2:15-3:15 Invited talk. PSDDs for Tractable Learning in Structured and Unstructured Spaces [Slides] Guy van den Broeck
3:15-3:35 Invited paper. Model Selection Scores for Multi-Relational Bayesian Networks Sajjad Gholami, Oliver Schulte, Vidhi Jian, Qiang Zhao
3:35-3:55 Invited paper. Learning to Learn Programs from Examples: Going Beyond Program Structure [Slides] Kevin Ellis, Sumit Gulwani
4:00-4:30 Coffee Break
4:30-5:30 Panel

Invited Speakers

Invited Papers

Accepted Papers


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.

Important Dates

Organizers Committee

  • Parisa Kordjamshidi
  • Tulane University, IHMC pkorjam@tulane.edu
  • Dan Roth
  • University of Illinois at Urbana-Champaign danr@illinois.edu
  • Jan-Willem Van den Meent
  • Northeastern University j.vandemeent@northeastern.edu
  • Dan Goldwasser
  • Purdue University dgoldwas@purdue.edu
  • Vibhav Gogate
  • University of Texas at Dallas vgogate@hlt.utdallas.edu
  • Kristian Kersting
  • Technical University of Dortmund kristian.kersting@cs.tu-dortmund.de

    Contact: delbp_2@googlegroups.com

    Program Committee

  • 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
  • Martin Mladenov
  • Technical University of Dortmund
  • Kai-Wei Chang
  • University of Virginia
  • Sebastian Riedel
  • University College London