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 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 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. 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.


Though 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 Summary

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


9:00-9:15 Workshop Overview. DeLBP aims and challenges Parisa Kordjamshidi
9:15-10:05 Keynote talk. Scruff: A Deep Probabilistic Cognitive Architecture [Slides] Avi Pfeffer
10:05-10:25 Accepted paper. Fairness-aware Relational Learning and Inference [Paper] Golnoosh Farnadi, Behrouz Babaki and Lise Getoor
10:30-11:00 Coffee Break
11:00-11:50 Keynote talk. Reading and Reasoning with Neural Program Interpreters [Slides] Sebastian Riedel
11:50-12:10 Accepted Paper. Image Classification Using Deep Learning and Prior Knowledge [Paper] Michelangelo Diligenti, Soumali Roychowdhury and Marco Gori
12:10-02:10 Lunch Break
2:10-03:00 Keynote talk. Probabilistic Logics and Declarative Statistical Learning [Slides] William Cohen
3:00-3:30 Invited Paper. Snorkel: Rapid Training Data Creation with Weak Supervision [Slides] Alex Ratner, Stephen H. Bach, Henry Ehrenberg, Jason Fries, Sen Wu, Christopher Ré
3:30pm-4:00 Coffee Break
4:00-4:50 Keynote talk. Pyro: Programmable Probabilistic Programming with Python and PyTorch Eli Bingham
4:50-5:30 Panel. Dan Roth, William Cohen, Kristian Kersting, Avi Pfeffer, Sebastian Riedel

Invited Speakers

Invited Papers

Accepted Papers


We 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.

Important Dates

Organizers Committee

  • Parisa Kordjamshidi
  • Tulane University, IHMC pkorjam@tulane.edu
  • Dan Roth
  • University of Pennsylvania danroth@seas.upenn.edu
  • Kristian Kersting
  • TU Darmstadt kersting@cs.tu-darmstadt.de
  • Dan Goldwasser
  • Purdue University dgoldwas@purdue.edu
  • Nikolaos Vasiloglou II
  • Ismion Inc nvasil@gmail.com

    Contact: delbp_3@googlegroups.com

    Program Committee

  • Guy Van den Broeck
  • University of California, Los Angeles
  • Nikolaos Vasiloglou
  • Ismion Inc
  • Sameer Singh
  • University of California, Irvine
  • 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 California, Los Angeles
  • Umar Manzoor
  • Tulane University
  • Mark Kaminski
  • University of Oxford
  • Avi Pfeffer
  • Charles River Analytics