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 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 Programing (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, and provide the ability to combine, chain and perform flexible inference with existing models and by exploiting first-order background knowledge.



9:00-9:30 Introductory Remarks Dan Roth
9:30-10:30 Invited Talk: The Democratization of Optimation Kristian Kersting
10:30-11:00 Break
11:00-12:00 Invited Talk: Probabilistic Soft Logic: A Scalable, Declarative Approach to Structured Prediction from Noisy Data Lise Getoor
12:00-12:20 Paper 1: Constructive Geometric Constraint Solving as a General Framework for KR-Based Commonsense Spatial Reasoning Carl Schultz and Mehul Bhatt
12:20-2:00 Lunch Break
2:00-2:20 Paper 2: JudgeD: a Probabilistic Datalog with Dependencies Brend Wanders, Maurice van Keulen and Jan Flokstra
2:20-2:40 Paper 3: On declarative modeling of structured pattern mining Tias Guns, Sergey Paramonov and Benjamin Negrevergne
2:40-3:00 Paper 4: Learning Constraints and Optimization Criteria Samuel Kolb
3:00-3:20 Paper 5: RELOOP: A Python-Embedded Declarative Language for Relational Optimization Martin Mladenov, Danny Heinrich, Leonard Kleinhans, Felix Gonsior and Kristian Kersting
3:20-4:10 Break + Demos paper1-demo (Carl Schultz); paper2-demo (Brend Wanders, JudgeD: the Mystery of the Phantom Flame); paper5-demo (Martin Mladenov, RELOOP); Wolfe-demo (Sameer Singh); Saul-demo (Parisa Kordjamshidi)
4:10-5:00 Panel

Invited Speakers

Accepted Papers


Contact: delbp-workshop-organizers@googlegroups.com