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SILO Seminar: Anthony Quinn, BE Ph.D.

Event Details

When: April 25, 2017, 12:30 PM

Location: 3rd Floor Orchard View Room , Discovery Building

Contact: 608-316-4401, hstampfli@wisc.edu

Anthony Quinn

Anthony Quinn

Randomized Distributional Design for Bayesian Transfer Learning

Video: https://vimeo.com/215556154

We characterize the Bayesian transfer learning problem as one of conditioning on external stochastic knowledge, typically a partially or completely specified distribution. The knowledge is ‘external’ in that a joint probability model specifying the stochastic dependence on this knowledge is not available. In consequence, there is no unique distributional design via standard Bayesian conditioning for transferring this knowledge. In this presentation, we adopt normative Bayesian decision making for this distributional design problem, modelling the unknown distribution hierarchically. This leads to Boltzmann-type relaxations of the classical maximum-entropy and minimum-cross-entropy designs. Among the consequences are (i) randomized designs in place of deterministic distributional estimates, and (ii) a mean-field-type relaxation of Bayes’ rule for transferring external distributions. Applications in coupled Kalman filers, and in centralized deliberation for sensor networks, are briefly reviewed.

Bio:   Anthony Quinn has been an associate professor in electronic and electrical engineering at Trinity College Dublin since 1993. Before that, he gained his primary degree from University College Dublin, and his PhD from the University of Cambridge. He is currently a one-year Fulbright visiting scholar in the Statistics Department at UC Berkeley. He specializes in Bayesian methods for problems in signal processing, dynamic system analysis and distributed decision-making. He is particularly interested in problems of robust model choice, deterministic distributional approximation, and distributional design in incompletely modelled contexts, with applications in Bayesian transfer learning and distributed knowledge processing.

SILO is a lecture series with speakers from the UW faculty, graduate students or invited researchers that discuss mathematical related topics. The seminars are organized by WID’s Optimization research group and sponsored by generous support of the Advance Technology Group of the 3M Company and the Analytics Group of Northwestern Mutual.
SILO’s purpose is to provide a forum that helps connect and recruit mathematically-minded graduate students. SILO is a lunch-and-listen format, where speakers present interesting math topics while the audience eats lunch.