[Ml-stat-talks] Reminder: CSML Seminar: Cynthia Rudin on Tuesday, October 20, 2015 at 12:30pm | Green Hall, Room 0-S-6

Barbara Engelhardt bee at princeton.edu
Mon Oct 19 09:21:48 EDT 2015


Sorry -- the talk is at 12:30pm.

On Mon, Oct 19, 2015 at 9:11 AM, Barbara Engelhardt <bee at princeton.edu>
wrote:

> Talk of interest tomorrow.
>
>
>
> Tuesday, October 20, 2015
>
> Green Hall, Room 0-S-6
>
> Cynthia Rudin- Massachusetts Institute of Technology
>
>
>
> *Title*: “Thoughts on Decision Making using Multi-Armed Bandits and
> Decision Trees”
>
>
>
> *Abstract*: This talk is comprised of two mini-talks about ongoing
> projects. This talk is a mix of storytelling, real-world multi-armed bandit
> applications, and a sort of revival for decision trees.
>
>
>
> * Mini-talk I: Regulating Greed Over Time for the Multi-Armed Bandit.
>
>
>
> I will describe our high scoring entry in the Exploration-Exploitation 3
> data mining competition. The goal of the competition was to build a better
> recommender system Yahoo!'s Front Page, which provides personalized new
> article recommendations. The main strategy we used was to carefully control
> the balance between exploiting good articles and exploring new ones in the
> multi-armed bandit setting. This strategy was based on an observation that
> there were broad trends over time in the click-through-rates of the
> articles. As it turns out, the types of broad trends we observe in the
> Yahoo! data are in fact pervasive throughout many types of real data (well
> beyond news articles). We thus formalize a setting where regulating greed
> over time can be provably beneficial. This is captured through regret
> bounds and leads to principled algorithms.
>
>
>
> * Mini-talk II: Scalable Bayesian Rule Lists
>
>
>
> I will discuss an approach to decision tree (rule list) learning that I
> have been pursuing over the past few years. This method does not have the
> disadvantage of greedy splitting and pruning that haunts decision tree
> algorithms. It yields very sparse logical models in a computationally
> efficient way. It is a fierce competitor for decision tree methods on a
> wide variety of problems, and much more principled.
>
>
>
> The work on multi-armed bandits is joint work with Stefano Traca, Ed Su,
> and Ta Chiraphadhanakul. The work on decision lists is joint with Ben
> Letham, David Madigan, Tyler McCormick, Hongyu Yang, and Margo Seltzer
>
>
>
>
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