26th International Conference on Inductive Logic Programming

4th - 6th September 2016, London




Invited Speakers

Professor David Jensen

Director of Knowledge Discovery Laboratory
College of Information and Computer Sciences
University of Massachusetts Amherst, USA

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Inferring Causal Models of Complex Relational and Dynamic Systems

Over the past 25 years, surprisingly effective techniques have been developed for inferring causal models from observational data. While traditional models reason about a given system by assuming that its behavior is stationary, causal models reason about how a system will behave under intervention. Unfortunately, nearly all existing methods for causal inference assume that data instances are independent and identically distributed, making them inappropriate for analyzing many social, economic, biological, and computational systems. In this talk, I will explain the key ideas, representations, and algorithms for causal inference, and I will describe very recent developments that extend those techniques to complicated systems with relational and dynamic behavior. I will describe practical methods for evaluating methods for causal inference and identify some of the most pressing research questions and new technical frontiers.

David Jensen is Professor of Computer Science at the University of Massachusetts Amherst. He serves as Director of the Knowledge Discovery Laboratory and Associate Director of the Computational Social Science Institute. He received his doctorate from Washington University in St. Louis in 1992. From 1991 to 1995, he served as an analyst with the Office of Technology Assessment, an agency of the United States Congress. His research focuses on machine learning and causal inference in complex data, with applications to social network analysis, computational social science, fraud detection, and management of large technical systems. He has served on the Executive Committee of the ACM Special Interest Group on Knowledge Discovery and Data Mining and on the program committees of many leading conferences, including the International Conference on Machine Learning, the International Conference on Knowledge Discovery and Data Mining, and the Conference on Uncertainty in Artificial Intelligence. He was a member of the 2006-2007 Defense Science Study Group, and served for six years on DARPA's Information Science and Technology (ISAT) Group. In 2011, he won the Outstanding Teaching Award from the UMass College of Natural Science.

Dr Vijay A. Saraswat

IBM TJ Watson Research Lab
New York, USA

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Machine Learning and Logic — the beginnings of a new computer science?

Our long-term research goal in Cognitive Computing Research at IBM is to develop systems that know deeply, learn continuously, reason with purpose and interact naturally. To further this agenda, we are focusing on a few deep domains. This talk will address the challenges of building cognitive assistants in compliance — assistants that deal with understanding and reasoning about the myriad (corporate, financial, privacy, ethical) laws and regulations within the context of which modern international businesses must operate. An interim goal for the compliance cognitive assistant is to clear the Uniform CPA exam, a professional certification attempted by master's level students. We will outline the tremendous technical challenges underlying this goal and our current approaches. We believe the key to achieving this goal is bringing together researchers in natural language understanding, machine learning, and knowledge representation/reasoning for a concerted attack on this problem.

Dr Vijay Saraswat joined IBM Research in 2003 after a year as a professor at Penn State, a couple of years at start-ups, and 13 years at Xerox PARC and AT&T Research. His main interests are in programming languages, constraints, logic, concurrency, and, now, machine learning.  In 2004, he founded and co-led the X10 project, a modern object-oriented programming language intended for scalable concurrent computing. In 2015, he was asked to join the Cognitive Computing Research division at TJ Watson, where he now guides long-term research in the New Computer Science — the confluence of natural language understanding, (deep) machine learning, and knowledge representation and reasoning. Vijay has collaborated extensively with colleagues across logic, AI, programming languages and systems; these collaborations have been recognized with an ACM Doctoral Dissertation award, a best-paper-in-20-years award from ALP, and a best-paper-in-10-years award from ACM. Vijay has a B Tech degree from IIT Kanpur, and an MS and PhD from Carnegie-Mellon University.

Dr Frank Wood

Department of Engineering Science
University of Oxford, UK

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Revolutionizing Decision Making, Democratizing Data Science, and Automating Machine Learning via Probabilistic Programming

Probabilistic programming aims to enable the next generation of data scientists to easily and efficiently create the kinds of probabilistic models needed to inform decisions and accelerate scientific discovery in the realm of big data and big models. Model creation and the learning of probabilistic models from data are key problems in data science. Probabilistic models are used for forecasting, filling in missing data, outlier detection, cleanup, classification, and scientific understanding of data in every academic field and every industrial sector. While much work in probabilistic modeling has been based on hand-built models and laboriously-derived inference methods, future advances in model-based data science will require the development of much more powerful automated tools than currently exist. In the absence of such automated tools, probabilistic models have traditionally co-evolved with methods for performing inference.  In both academic and industrial practice, specific modeling assumptions are made not because they are appropriate to the application domain, but because they are required to leverage existing software packages or inference methods. This intertwined nature of modeling and computation leaves much of the promise of probabilistic modeling out of reach for even expert data scientists. The emerging field of probabilistic programming will reduce the technical and cognitive overhead associated with writing and designing novel probabilistic models by both introducing a programming (modeling) language abstraction barrier and automating inference. The automation of inference, in particular, will lead to massive productivity gains for data scientists, much akin to how high-level programming languages and advances in compiler technology have transformed software developer productivity. What is more, not only will traditional data science be accelerated, but the number and kind of people who can do data science also will be dramatically increased.  My talk will touch on all of this, explain how to develop such probabilistic programming languages, highlight some exciting ways such languages are starting to be used, and introduce what I think are some of the most important challenges facing the field as we go forward.

Dr. Wood is an associate professor in the Department of Engineering Science at the University of Oxford. Before that Dr. Wood was an assistant professor of Statistics at Columbia University and a research scientist at the Columbia Center for Computational Learning Systems. He formerly was a postdoctoral fellow of the Gatsby Computational Neuroscience Unit of the University College London under Dr. Yee Whye Teh. He received his PhD from Brown University in computer science under the supervision of Dr. Michael Black and Dr. Tom Griffiths. Prior to his academic career he was a successful entrepreneur having run and sold the content-based image retrieval company ToFish! to Time Warner and serving as CEO of Interfolio. He started his career working at both the Cornell Theory Center and subsequently the Lawrence Berkeley National Laboratory. Dr. Wood holds 6 patents, has authored over 40 papers, received the AISTATS best paper award in 2009, and has been awarded faculty research awards from Xerox, Google and Amazon.