EAGER: IIS: Empowering Probabilistic Reasoning with Random Projections

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Autonomous agents such as self-driving cars are required to act intelligently and adaptively in increasingly complex and uncertain real-world environments. To cope with the uncertainty and ambiguity of real world domains, AI systems rely heavily on statistical approaches. To make sensible decisions under uncertainty, agents need to reason probabilistically about their environments. Probabilistic reasoning, however, is known to be computationally very difficult in the worst case. While significant progress has been made over the past decades, many complex problems remain out of reach. This project aims to develop a new family of algorithms for reasoning under uncertainty. These novel techniques have the potential to provide more efficient algorithms for decision-making, learning and inference with improved theoretical guarantees on the accuracy. These techniques will be applicable in a wide range of domains, including medical diagnosis, information extraction, computer vision, and robotics.This research project will develop a new family of algorithms for reasoning under uncertainty based on random projections. Random projections have played a key role in scaling up data mining and database systems. While drastically reducing computational cost, they also provide principled approximations. This research will explore the use of random projections based on universal hashing schemes in the context of probabilistic reasoning. The project will develop new techniques for learning and decision making under uncertainty problems. Specifically, new frameworks and algorithms with improved theoretical guarantees and practical performance will be developed. In order to provide efficient reasoning algorithms, the use of random projections will be considered in combination with a range of existing techniques, including modern optimization, variational, and sampling methods. A key focus will be to develop practical techniques and scale-up to real-world domains. The techniques developed will be made available to both academia and industry through open-source software. Educational and outreach efforts will include the involvement of undergraduate students undertaking independent research projects.