[Ml-stat-talks] Joint CS/CSML Seminar: Shie Mannor (Technion) on Tuesday, October 13 at 12:30pm | Computer Science Room 105

Barbara Engelhardt bee at princeton.edu
Mon Oct 5 18:25:16 EDT 2015

Correction: next Tuesday.

On Mon, Oct 5, 2015 at 3:45 PM, Barbara Engelhardt <bee at princeton.edu>

> Talk of interest tomorrow.
> ---------- Forwarded message ----------
> From: Esther K. Ruder <estherk at princeton.edu>
> Date: Mon, Oct 5, 2015 at 3:34 PM
> Subject: Joint CS/CSML Seminar: Shie Mannor (Technion) on Tuesday, October
> 13 at 12:30pm | Computer Science Room 105
> To: CSML-seminars at princeton.edu
> Tuesday, October 13, 2015
> Computer Science, Room 105
> 12:30pm
> Shie Mannor, Technion – Israel Institute of Technology
> Title: Actionable big data: from data to decisions and back
> Abstract: In many sequential decision problems all that we have is a
> record of historical trajectories. Building dynamic models from these
> trajectories and ultimately sequential decision policies may result in much
> uncertainty and bias. In this talk we consider the question of how to
> create control policies from existing historical data and how to better
> sample trajectories so that future control policies would be better. This
> question has been central in reinforcement learning in the last decade if
> not more, and involves methods from statistics, machine learning,
> optimization, and control theory.
> I will start my talk with demonstrating why planning with parameter
> uncertainty is an important issue. I will then describe several approaches:
> Bayesian uncertainty model over the unknown parameters, a robust approach
> that takes a worst case view, and a frequentist approach. I will then
> discuss the challenges that are posed when the model itself rather than
> just the parameters may not be fully known.
> I will then describe two challenging real-world domains that have been
> studied in my research group in collaboration with experts from industry
> and academia: diabetes care management in healthcare and asset management
> in high-voltage transmission grids. For each domain I will describe our
> efforts to reduce the problem to its bare essentials as a reinforcement
> learning problem, the algorithms for learning the control policies, and
> some of the lessons we learned.
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