UAI 2017 - Workshops

Statistical Relational AI (StarAI)

Organizers: Angelika Kimmig, David Poole, Jay Pujara, Tim Rocktäschel

The purpose of the Statistical Relational AI (StarAI) workshop is to bring together researchers and practitioners from two fields: logical (or relational) AI and probabilistic (or statistical) AI. These fields share many key features and often solve similar problems and tasks. Until recently, however, research in them has progressed independently with little or no interaction. The fields often use different terminology for the same concepts and, as a result, keeping-up and understanding the results in the other field is cumbersome, thus slowing down research. Our long term goal is to change this by achieving a synergy between logical and statistical AI.
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Causality: Learning, Inference, and Decision-Making

Organizers: Elias Bareinboim, Kun Zhang, Caroline Uhler, Jiji Zhang, Dominik Janzing

Causality shapes how we view, understand, and react to the world around us. It’s a key ingredient in building AI systems that are autonomous and can act efficiently in complex and uncertain environments. It’s also important to the process of scientific discovery since it underpins how explanations are constructed and the scientific method. Not surprisingly, the tasks of learning and reasoning with causal-effect relationships have attracted great interest in the artificial intelligence and machine learning communities. This effort has led to a very general theoretical and algorithmic understanding of what causality means and under what conditions it can be inferred. These results have started to percolate through more applied fields that generate the bulk of the data currently available, ranging from genetics to medicine, from psychology to economics. This one-day workshop will explore causal inference in a broad sense through a set of invited talks, open problems sessions, presentations, and a poster session. In this workshop, we will focus on the foundational side of causality on the one hand, and challenges presented by practical applications on the other. By and large, we welcome contributions from all areas relating to the study of causality.
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Bayesian Modelling Applications

Organizers: John Mark Agosta, Tomas Singliar

BMAW 2017 aims to foster discussion on the challenges of building working applications of probabilistic methods while considering stakeholders, user interaction, tools, knowledge elicitation, learning, validation, system integration, and deployment. This year we especially appreciate the degree to which the practicality of sharing application development experience comes down to sharing reusable software. The workshop will correspondingly feature real-world Bayesian applications, where teams will explain what tools they adopted to facilitate their work. We will focus the discussion on best practices and tools for sharing data, code and models in easily reproducible ways.
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