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SUMMARY:CS Colloquium: Jonas Mueller (MIT) – Learning Optimal Interventions under Uncertainty
DESCRIPTION:Speaker: Jonas Mueller, MIT
Talk Title: Learning Optimal Interventions under Uncertainty
Series: Computer Science Colloquium
Abstract: A basic goal of data analysis is learning which actions (ie. interventions) are best for producing desired outcomes. While advances in reinforcement learning and bandit/Bayesian optimization have shown great promise, these sequential methods are primarily limited to digital environments where iterating between modeling and experimentation is easy. Although more widely applicable, learning from a fixed (observational) dataset will inherently involve substantial uncertainty due to limited samples, and it is undesirable to prescribe actions whose outcomes are unclear. \n
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In this talk, I will consider such settings from a Bayesian perspective and formalize the of role of uncertainty in data-driven actions. Adopting a Gaussian process framework, I will introduce a conservative definition of the optimal intervention which can be either tailored on an individual basis or globally enacted over a population. Subsequently, these ideas are extended to structured sequence data via a recurrent variational autoencoder model. In both cases, gradient methods are employed to identify the best intervention and a key theme of the approach is carefully constraining this optimization to avoid regions of high uncertainty. Various applications of this methodology will presented including gene expression manipulation, therapeutic antibody design, and revision of natural language.\n
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This lecture satisfies requirements for CSCI 591: Research Colloquium. Please note, due to limited capacity in OHE 100D, seats will be first come first serve.\n
Biography: Jonas Mueller is a Computer Science Ph.D. student at MIT working with Tommi Jaakkola and David Gifford. His research interests lie in developing machine learning methods to advance both statistical science and artificial intelligence applications. Integrating ideas from optimal transport, deep learning, Bayesian/bandit optimization, and interpretable modeling, much of his work has been motivated by applications in bioinformatics and natural language processing. Previously, Jonas studied Math and Statistics at UC Berkeley, where he was awarded the Departmental Citation, and he recently also spent some time at Microsoft Research.\n
Host: Computer Science Department
DTSTART:20180308T110000
LOCATION:OHE 100D
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DTEND:20180308T120000
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