Learning and Incentives in Systems with Humans in the Loop

Speaker:        Dr. Chien-Ju HO
                Cornell University

Title:          "Learning and Incentives in Systems with Humans
                 in the Loop"

Date:           Monday, 20 March 2017

Time:           4:00pm - 5:00pm

Venue:          Lecture Theater F (near lift nos. 25/26), HKUST

Abstract:

There is an increasing amount of human-generated data available on the
internet -- including online reviews, user search histories, datasets
labeled using crowdsourcing, and beyond. This has created an unprecedented
opportunity for researchers in machine learning and data science to
address a wide range of problems. On the other hand, human-generated data
also creates unique challenges. Humans might be strategic or careless,
possess diverse skills, or have behavioral biases. What is the right way
to understand and utilize human-generated data? Furthermore, can we better
design the systems with humans in the loop to generate more useful data in
the first place?

In this talk, I will present my research which addresses the challenges in
utilizing and eliciting data from humans. In particular, I will introduce
the problem of actively purchasing data from humans for solving machine
learning tasks, and demonstrate how to convert a large class of machine
learning algorithms into pricing and learning mechanisms. I will also
discuss how to obtain high-quality data from humans using financial
incentives and present our findings in a comprehensive set of behavioral
experiments conducted on Amazon Mechanical Turk.


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Biography:

Chien-Ju is a postdoctoral associate at Cornell University. He obtained
his Ph.D. in Computer Science from UCLA in 2015, advised by Jenn Wortman
Vaughan. He also spent three years visiting the EconCS group at Harvard
from 2012 to 2015, hosted by Yiling Chen. His research interests are in
machine learning, algorithmic economics, online behavioral social science,
crowdsourcing, and artificial intelligence. His dissertation was on the
design and analysis of crowdsourcing mechanisms. He is the recipient of
the Google Outstanding Graduate Research Award at UCLA in 2015. His work
was nominated for Best Paper Award at WWW 2015.