DeLBP 2017

Overview

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

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

Topics Summary

• 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.

Schedule

Invited Speakers

• Guy Van den Broeck, University of California, Los Angeles.

  Title: PSDDs for Tractable Learning in Structured and Unstructured Spaces

  Abstract: In this talk, I will discuss two related settings for learning a probabilistic model from data. In the first setting, called tractable learning, we are given a standard dataset and the goal is to learn a distribution that allows for efficient inference. This setting received a lot of attention recently, and is often tackled by learning a circuit representation of the distribution. In the second setting, which has not been treated as systematically before, one has access to Boolean constraints that characterize examples known to be impossible (e.g., due to known domain physics). The task is then to learn a probabilistic model over this structured space that is guaranteed to assign a zero probability to each impossible example. I will describe a new class of Arithmetic Circuits, the PSDD, for addressing both classes of learning problems. The PSDD is based on advances from both machine learning and logical reasoning and can be learned under Boolean constraints. It achieves state-of-the-art results, both in tractable learning from standard datasets, and in structured probability spaces, such as rankings and combinatorial objects. I will additionally show how PSDDs can be learned from a new type of incomplete datasets, in which examples are partially specified using arbitrary Boolean expressions.

  Bio: Guy Van den Broeck is an Assistant Professor and Samueli Fellow in the Computer Science Department at the University of California, Los Angeles (UCLA). Guy’s research interests are in artificial intelligence, machine learning, logical and probabilistic automated reasoning, and statistical relational learning. He also studies applications of reasoning in other fields, such as probabilistic databases and programming languages. Guy’s work received best paper awards from key artificial intelligence venues such as UAI, ILP, and KR, and an outstanding paper honorable mention at AAAI. His doctoral thesis was awarded the ECCAI Dissertation Award for the best European dissertation in AI. He directs the Statistical and Relational Artificial Intelligence (StarAI) Lab at UCLA.

• Nikolaos Vasiloglou, LogicBlox.

  Title: Declarative Data Science

  Abstract: abstract: Data science has become essential for the operations of every business. Unfortunately data science follows the excel syndrome when it is seen from the database perspective. Although the data is usually normalized in a relational database, when it is time to be used in a machine learning model denormalization is required in ordered to be fed to the algorithms. This widely adopted practice has two problems. First it can convert a small data problem to a big data problem and second it ignores the relational nature of the data that might be important for the machine learning model. In this talk we will see how we can develop fast algorithms inside the database by leveraging the relational model. Based on some recent join algorithms we show that even with the database overhead it is much faster to process the data inside the database rather than exporting it and using high performance computing. We also demonstrate how we can use Linear Programming as a framework in doing data science inside the database and how this can supercharge the process of model development.

• Tias Guns, Vrije Universiteit Brussel.

Declarative constraint-based pattern mining: from modeling to solving

Abstract: In todays data-rich world, pattern mining techniques allow us to extract knowledge from data. However, such knowledge can take many forms and often depends on the application at hand. This calls for generic techniques that can be used in a wide range of settings. Declarative constraint-based mining offers an answer to this question. By building on constraint programming solvers, a generic method that fits many pattern mining settings is obtained. However, there is a trade-off between generality and efficiency. This talk will discuss multiple efforts to reduce the trade-off, both at the modeling level and at the solving level.

Bio: Tias Guns is Assistant Professor at the Vrije Universiteit Brussel (VUB), in the Business, Technology and Operations lab of the faculty of Economic and Social Sciences & Solvay Business School. His research lies on the border between data mining and constraint programming, and his main interest is in integrating domain expertise and user constraints into data analytics tasks. As part of his PhD, he has developed the CP4IM framework which showed for the first time the potential of using constraint programming for pattern mining. His PhD was awarded with both the constraint programming dissertation award and the ECCAI artificial intelligence dissertation award. He is an active member of the community and has organized a number of workshops and a special issue on the topic of combining constraint programming with machine learning and data mining.

Invited Papers

• Learning to Learn Programs from Examples: Going Beyond Program Structure. Kevin Ellis, Sumit Gulwani. [Download]
• Foundations of Declarative Data Analysis Using Limit Datalog Programs. Mark Kaminski, Bernardo Cuenca Grau, Egor Kostylev, Boris Motik, Ian Horrocks. [Download]
• Model Selection Scores for Multi-Relational Bayesian Networks. Sajjad Gholami, Oliver Schulte, Vidhi Jian, Qiang Zhao. [Download]

Accepted Papers

• Scaffolding the Generation of Machine Learning Models with SciRise, Aneesha Bakharia. [Download]
• Data Science with Linear Programming, Nantia Makrynioti, Nikolaos Vasiloglou, Emir Pasalic and Vasilis Vassalos. [Download]

Submissions

Important Dates

• Submission Deadline: May 8th May 20th, 2017
• Notification: June 5th June 9th, 2017
• Workshop Days: August 19th, 2017

Organizers Committee

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