Jun 14, 2019
"An important component of human problem-solving expertise is the ability to use knowledge about solving easy problems to guide the solution of difficult ones.” - Minsky A longstanding intuition in AI is that intelligent agents should be able to use solutions to easy problems to solve hard problems. This has often been termed the “tractable island paradigm.” How do we act on this intuition in the domain of probabilistic reasoning? This talk will describe the status of probabilistic reasoning algorithms that are driven by the tractable islands paradigm when solving optimization, likelihood and mixed (max-sum-product, e.g. marginal map) queries. I will show how heuristics generated via variational relaxation into tractable structures, can guide heuristic search and Monte-Carlo sampling, yielding anytime solvers that produce approximations with confidence bounds that improve with time, and become exact if enough time is allowed.
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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