Jenn Wortman Vaughan

Overviews

Mathematical Foundations for Social Computing (full text at CACM, related CCC white paper)
Yiling Chen, Arpita Ghosh, Michael Kearns, Tim Roughgarden, and Jennifer Wortman Vaughan
Communications of ACM, Volume 9, Number 12, pages 102-108, December 2016
Making Better Use of the Crowd (Draft PDF, comments welcome!)
Jennifer Wortman Vaughan
Survey/position paper/best practice guide written to accompany the NIPS tutorial on Crowdsourcing: Beyond Label Generation, December 2016
The Inescapability of Uncertainty: AI, Uncertainty, and Why You Should Vote No Matter What Predictions Say (Medium link)
Jennifer Wortman Vaughan and Hanna Wallach
In Data & Society: Points, October 2016
(Short version appeared at the CHI 2017 Workshop on Designing for Uncertainty in HCI)

Design and Analysis of Prediction Markets and Other Wagering Mechanisms

Incentive-Compatible Forecasting Competitions (coming soon)
Jens Witkowski, Rupert Freeman, Jennifer Wortman Vaughan, David Pennock, and Andreas Krause
To appear in the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018)
A Decomposition of Forecast Error in Prediction Markets (working paper on arxiv)
Miroslav Dudík, Sébastien Lahaie, Ryan Rogers, and Jennifer Wortman Vaughan
To appear in Advances in Neural Information Processing Systems 30 (NIPS 2017)
The Double Clinching Auction for Wagering (PDF)
Rupert Freeman, David M. Pennock, and Jennifer Wortman Vaughan
In the 18th ACM Conference on Economics and Computation (EC 2017)
Bounded Rationality in Wagering Mechanisms (PDF)
David M. Pennock, Vasilis Syrgkanis, and Jennifer Wortman Vaughan
In the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016)
The Possibilities and Limitations of Private Prediction Markets (PDF)
Rachel Cummings, David M. Pennock, and Jennifer Wortman Vaughan
In the 17th ACM Conference on Economics and Computation (EC 2016)
Belief Aggregation with Automated Market Makers (SSRN)
Rajiv Sethi and Jennifer Wortman Vaughan
Computational Economics, Volume 48, Issue 1, Pages 155-178, 2016
Integrating Market Makers, Limit Orders, and Continuous Trade in Prediction Markets (PDF)
Hoda Heidari, Sébastien Lahaie, David Pennock, and Jennifer Wortman Vaughan
In the Sixteeth ACM Conference on Economics and Computation (EC 2015)
An Axiomatic Characterization of Wagering Mechanisms (preprint)
Nicolas S. Lambert, John Langford, Jennifer Wortman Vaughan, Yiling Chen, Daniel Reeves, Yoav Shoham, and David M. Pennock
Journal of Economic Theory, Volume 156, Pages 389-416, 2015
(Mostly supersedes the EC 08 version)
Market Making with Decreasing Utility for Information (PDF)
Miroslav Dudík, Rafael Frongillo, and Jennifer Wortman Vaughan
In the 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014)
A General Volume-Parameterized Market Making Framework (PDF)
Jacob Abernethy, Rafael Frongillo, Xiaolong Li, and Jennifer Wortman Vaughan
In the Fifteenth ACM Conference on Economics and Computation (EC 2014)
Removing Arbitrage from Wagering Mechanisms (PDF)
Yiling Chen, Nikhil R. Devanur, David Pennock, and Jennifer Wortman Vaughan
In the Fifteenth ACM Conference on Economics and Computation (EC 2014)
An Axiomatic Characterization of Adaptive-Liquidity Market Makers (PDF)
Xiaolong Li and Jennifer Wortman Vaughan
In the Fourteenth ACM Conference on Electronic Commerce (EC 2013)
(A preliminary version appeared in the ICML 2012 Workshop on Markets, Mechanisms, and Multi-Agent Models)
Cost Function Market Makers for Measurable Spaces (PDF)
Yiling Chen, Michael Ruberry, and Jennifer Wortman Vaughan
In the Fourteenth ACM Conference on Electronic Commerce (EC 2013)
Efficient Market Making via Convex Optimization, and a Connection to Online Learning (preprint)
Jacob Abernethy, Yiling Chen, and Jennifer Wortman Vaughan
ACM Transactions on Economics and Computation, Volume 1, Number 2, Article 12, May 2013
(Supersedes the EC 10 and EC 11 papers)
Designing Informative Securities (PDF)
Yiling Chen, Mike Ruberry, and Jennifer Wortman Vaughan
In the 28th Conference on Uncertainty in Artificial Intelligence (UAI 2012)
An Optimization-Based Framework for Automated Market-Making (PDF)
Jacob Abernethy, Yiling Chen, and Jennifer Wortman Vaughan
In the Twelfth ACM Conference on Electronic Commerce (EC 2011)
(A preliminary version appeared in the NIPS 2010 Workshop on Computational Social Science and the Wisdom of Crowds)
A New Understanding of Prediction Markets Via No-Regret Learning (PDF)
Yiling Chen and Jennifer Wortman Vaughan
In the Eleventh ACM Conference on Electronic Commerce (EC 2010)
Connections Between Markets and Learning (PDF)
Yiling Chen and Jennifer Wortman Vaughan
In ACM SIGecom Exchanges, Volume 9, Number 1, June 2010
(Shorter synapse of the EC 2010 paper)
Complexity of Combinatorial Market Makers (PDF)
Yiling Chen, Lance Fortnow, Nicolas Lambert, David Pennock, and Jennifer Wortman
In the Ninth ACM Conference on Electronic Commerce (EC 2008)
Self-Financed Wagering Mechanisms for Forecasting (PDF)
Nicolas Lambert, John Langford, Jennifer Wortman, Yiling Chen, Daniel Reeves, Yoav Shoham, and David Pennock
In the Ninth ACM Conference on Electronic Commerce (EC 2008)
Winner of an Outstanding Paper Award at EC
(A preliminary version appeared in the DIMACS Workshop on the Boundary Between Economic Theory and CS)

Design and Analysis of Crowdsourcing Markets

The Communication Network Within the Crowd (PDF)
Ming Yin, Mary Gray, Siddharth Suri, and Jennifer Wortman Vaughan
In the Twenty-Fifth International World Wide Web Conference (WWW 2016)
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems (Publisher's Page)
Chien-Ju Ho, Aleksandrs Slivkins, and Jennifer Wortman Vaughan
Journal of Artificial Intelligence Research, Volume 55, Pages 317-359, 2016
(Supersedes the EC 14 paper)
Incentivizing High Quality Crowdwork (PDF)
Chien-Ju Ho, Aleksandrs Slivkins, Siddharth Suri, and Jennifer Wortman Vaughan
In the Twenty-Fourth International World Wide Web Conference (WWW 2015)
Nominee for Best Paper Award at WWW
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems (extended version)
Chien-Ju Ho, Aleksandrs Slivkins, and Jennifer Wortman Vaughan
In the Fifteenth ACM Conference on Economics and Computation (EC 2014)
Online Decision Making in Crowdsourcing Markets: Theoretical Challenges (Position Paper) (PDF)
Aleksandrs Slivkins and Jennifer Wortman Vaughan
In ACM SIGecom Exchanges, Volume 12, Number 2, December 2013
Adaptive Task Assignment for Crowdsourced Classification (PDF)
Chien-Ju Ho, Shahin Jabbari, and Jennifer Wortman Vaughan
In the 30th International Conference on Machine Learning (ICML 2013)
(Simultaneously appeared in the ACM EC 3rd Workshop on Social Computing and User Generated Content)
Online Task Assignment in Crowdsourcing Markets (PDF)
Chien-Ju Ho and Jennifer Wortman Vaughan
In the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2012)

Online Learning, Active Learning, and Evolution

Oracle-Efficient Learning and Auction Design (working paper on arxiv)
Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, and Jennifer Wortman Vaughan
To appear in the 58th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2017)
Evolution with Drifting Targets (PDF)
Varun Kanade, Leslie G. Valiant, and Jennifer Wortman Vaughan
In the 23rd Annual Conference on Learning Theory (COLT 2010)
Regret Minimization with Concept Drift (PDF)
Koby Crammer, Eyal Even-Dar, Yishay Mansour, and Jennifer Wortman Vaughan
In the 23rd Annual Conference on Learning Theory (COLT 2010)
The True Sample Complexity of Active Learning (Publisher's Page)
Maria-Florina Balcan, Steve Hanneke, and Jennifer Wortman Vaughan
Machine Learning Journal (Special issue on COLT 2008), Volume 80, Numbers 2-3, Pages 111-139, 2010
(Supersedes the COLT 08 version)
Censored Exploration and the Dark Pool Problem (PDF)
Kuzman Ganchev, Michael Kearns, Yuriy Nevmyvaka, and Jennifer Wortman Vaughan
In the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009)
Winner of the Best Student Paper Award at UAI
(Also appeared in Communications of the ACM, Research Highlights, May 2010 (Online Issue))
Regret to the Best Vs. Regret to the Average (Publisher's Page)
Eyal Even-Dar, Michael Kearns, Yishay Mansour, and Jennifer Wortman
Machine Learning Journal (Special issue on COLT 2007), Volume 72, Numbers 1-2, Pages 21-37, 2008
(Supersedes the COLT 07 version)
The True Sample Complexity of Active Learning (PDF)
Maria-Florina Balcan, Steve Hanneke, and Jennifer Wortman
In the 21st Annual Conference on Learning Theory (COLT 2008)
Winner of the Mark Fulk Best Student Paper Award at COLT
(A preliminary version appeared in the NIPS 2007 Workshop on Principles of Learning Problem Design)
Exploration Scavenging (PDF)
John Langford, Alexander Strehl, and Jennifer Wortman
In the 25th International Conference on Machine Learning (ICML 2008)
Regret to the Best Vs. Regret to the Average (PDF)
Eyal Even-Dar, Michael Kearns, Yishay Mansour, and Jennifer Wortman
In the 20th Annual Conference on Learning Theory (COLT 2007)
Winner of a Best Student Paper Award at COLT
(A preliminary version appeared in the NIPS 2006 Workshop on Online Trading of Exploration and Exploitation)
Risk-Sensitive Online Learning (Corrected version, October 2006: PDF)
Eyal Even-Dar, Michael Kearns, and Jennifer Wortman
In the 17th International Conference on Algorithmic Learning Theory (ALT 2006)

Domain Adaptation and Multi-Source Learning

A Theory of Learning from Different Domains (Publisher's PDF)
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan
Machine Learning Journal (Special Issue on Learning from Multiple Sources), Volume 79, Numbers 1-2, Pages 151-175, 2010
(Supersedes the NIPS 07 paper)
Learning from Multiple Sources (Publisher's PDF)
Koby Crammer, Michael Kearns, and Jennifer Wortman
Journal of Machine Learning Research, Volume 9, Pages 1757-1774, 2008
(Supersedes the NIPS 06 paper)
Learning Bounds for Domain Adaptation (PDF)
John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman
In Advances in Neural Information Processing Systems 20 (NIPS 2007)
Learning from Multiple Sources (PDF)
Koby Crammer, Michael Kearns, and Jennifer Wortman
In Advances in Neural Information Processing Systems 19 (NIPS 2006)
Learning from Data of Variable Quality (PDF)
Koby Crammer, Michael Kearns, and Jennifer Wortman
In Advances in Neural Information Processing Systems 18 (NIPS 2005)

Social Networks and Collective Behavior

Behavioral Experiments on Biased Voting in Networks (Publisher's PDF)
Michael Kearns, Stephen Judd, Jinsong Tan, and Jennifer Wortman
Proceedings of the National Academy of Sciences, Volume 106, Number 5, Pages 1347-1352, 2009
Learning from Collective Behavior (PDF)
Michael Kearns and Jennifer Wortman
In the 21st Annual Conference on Learning Theory (COLT 2008)
Viral Marketing and the Diffusion of Trends on Social Networks (PDF)
Jennifer Wortman
University of Pennsylvania Technical Report MS-CIS-08-19, May 2008
In fulfillment of the Department of Computer and Information Science Written Preliminary Exam II
Privacy-Preserving Belief Propagation and Sampling (PDF)
Michael Kearns, Jinsong Tan, and Jennifer Wortman
In Advances in Neural Information Processing Systems 20 (NIPS 2007)
Winner of the Best Student Paper Award at the New York Academy of Sciences 2007 Symposium on ML

Computer Science and Game Theory

Maintaining Equilibria During Exploration in Sponsored Search Auctions (Publisher's Page)
John Langford, Lihong Li, Yevgeniy Vorobeychik, and Jennifer Wortman
Algorithmica, Volume 58, Number 4, Pages 990-1021, 2010
(Supersedes the WINE 07 paper)
Maintaining Equilibria During Exploration in Sponsored Search Auctions (PDF)
Jennifer Wortman, Yevgeniy Vorobeychik, Lihong Li, and John Langford
In the 3rd International Workshop on Internet and Network Economics (WINE 2007)
Sponsored Search with Contexts (PDF)
Eyal Even-Dar, Michael Kearns, and Jennifer Wortman
In the 3rd International Workshop on Internet and Network Economics (WINE 2007)
(This longer version appeared in the WWW 2007 Third Workshop on Sponsored Search Auctions)
Run the GAMUT: A Comprehensive Approach to Evaluating Game-Theoretic Algorithms (PDF; GAMUT website)
Eugene Nudelman, Jennifer Wortman, Yoav Shoham, and Kevin Leyton-Brown
In the 3rd International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2004)
(Short versions appeared at the Second World Congress of the Game Theory Society and the 15th Annual Conference on Game Theory)

Ph.D. Thesis

Learning from Collective Preferences, Behavior, and Beliefs (PDF)
Jennifer Wortman Vaughan
Doctoral Dissertation, University of Pennsylvania, 2009