Overview

This page describes work supported by the National Science Foundation under Grant No. IIS 1054911.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Basic Project Information

Award title: CAREER: Learning- and Incentives-Based Techniques for Aggregating Community-Generated Data

Duration: June 1, 2011 - March 31, 2015

PI and Primary Contact: Jennifer Wortman Vaughan

Students supported: Chien-Ju Ho, Shahin Jabbari

Project Overview

The Internet has led to the availability of novel sources of data on the preferences, behaviors, and beliefs of massive communities of users. Both researchers and engineers are eager to aggregate and interpret this data. However, websites sometimes fail to incentivize high-quality contributions, leading to variable quality data. Furthermore, assumptions made by traditional theories of learning break down in these settings.

This project seeks to create foundational machine learning models and algorithms to address and explain the issues that arise when aggregating local beliefs across large communities, and to advance the state-of-the-art understanding of how to motivate high quality contributions. The research can be split into three directions:

1. Developing mathematical foundations and algorithms for learning from community-labeled data. This direction involves developing learning models for data from disparate (potentially self-interested or malicious) sources and using insight from these models to design efficient learning algorithms.
2. Understanding and designing better incentives for crowdsourcing. This direction involves modeling crowdsourcing contributions to determine which features to include in systems to encourage the highest quality contributions.
3. Introducing novel economically-motivated mechanisms for opinion aggregation. This involves formalizing the properties a prediction market should satisfy and making use of ideas from machine learning and optimization to derive tractable market mechanisms satisfying these properties.

Research Papers

Educational Activities and Broader Impact

Courses created:

• Mathematical Frameworks for Social Computing (UCLA, Winter 2012)

Related workshops organized:

• Workshop on Crowdsourcing: Theory, Algorithms, and Applications at NIPS 2013
• Workshop on Markets, Mechanisms, and Multi-Agent Models at ICML 2012
• Second Workshop on Computational Social Science and the Wisdom of Crowds at NIPS 2011

Related tutorial presentations:

• Tutorials on Prediction, Belief, and Markets at ICML 2012, KDD 2012, and AAAI 2013
• Tutorial on Learning and Markets at Machine Learning Summer School, UC Santa Cruz, 2012

PI's primary outreach activities:

• Program Co-chair, Celebration of Women in Computing in Southern California 2012
• Executive Board Member, Workshop for Women in Machine Learning (through 2012 and again starting 2014)