Subscribe to DSC Newsletter

Free Livestream: Bayesian Optimization From A/B to A-Z Testing & AI Debate Wed. Aug. 27 6:00pm MDT

Event Details

Free Livestream: Bayesian Optimization From A/B to A-Z Testing & AI Debate Wed. Aug. 27 6:00pm MDT

Time: August 27, 2014 from 6pm to 9pm
Location: Oracle Campus
Street: Bldg 1 - 500 Eldorado Blvd
City/Town: Broomfield, CO 80021
Website or Map: http://www.meetup.com/Data-Sc…
Event Type: free, and, open, to, all, -, livestream
Organized By: Michael Walker
Latest Activity: Aug 28, 2014

Export to Outlook or iCal (.ics)

Event Description

RSVP at: http://bit.ly/1lnm2VT

Oracle Campus - Wednesday August 27, 2014 @ 6:00pm MDT

 

NOTE: For folks unable to attend in person register and we will email you a livestream link 2 hours prior to event.

 

Location: Oracle Campus, Bldg 1 - 500 Eldorado Blvd, Broomfield, CO 80021 Map: https://goo.gl/maps/KhWc6

  

Agenda:

6:00 - 6:20 Schmooze - Food shall be served in Lobby

6:20 - 6:30 Announcements

6:30 - 7:45 Bayesian Optimization: From A/B to A-Z Testing by Michael Mozer

7:45 - 8:30 Artificial Intelligence Debate

8:30 - 9:00 Networking

 

Bayesian Optimization: From A/B to A-Z Testing - Abstract

 

A/B testing is a traditional method of conducting a randomized controlled experiment to compare the effect of two treatments, A and B, on human subjects. For example, two alternative banner ads may be served to evaluate which is more effective in driving click throughs. A/B testing is used not only for marketing and web design but is the dominant paradigm in the experimental behavioral sciences used to understand human learning, reasoning, and decision making. Although the method can be extended to compare a handful of treatments, it does not solve the problem one often faces: searching over a large, possibly combinatorial or continuous space of alternatives to identify the treatment that achieves the best outcome. We describe a solution to this problem using Gaussian process surrogate-based optimization, a Bayesian method that relies on generative probabilistic models of human choice and judgment. Instead of assigning many human subjects to each of a few of treatments, the technique evaluates a few subjects on each of many treatments. The technique leverages structure in the space of treatments to infer the function that relates treatment to outcomes. We show the efficiency and accuracy of the technique on a range of problems, including: identifying preferred color combinations, maximizing charitable donations, and improving student learning of facts and concepts. This work is in collaboration with Robert Lindsey (University of Colorado) and Harold Pashler (UC San Diego).

 

RSVP at: http://bit.ly/1lnm2VT

Comment Wall

Comment

RSVP for Free Livestream: Bayesian Optimization From A/B to A-Z Testing & AI Debate Wed. Aug. 27 6:00pm MDT to add comments!

Join Data Science Central

Attending (2)

Videos

  • Add Videos
  • View All

© 2019   Data Science Central ®   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service