Navigation
  • About
  • People
  • Publications
  • Blog
  • Teaching
  • Student Projects
  • Openings
  • Contact
  • About
  • People
  • Publications
  • Blog
  • Teaching
  • Student Projects
  • Openings
  • Contact

Crowds, Learning and Incentives

How can machines and people work together to accomplish what neither can alone? Our research focuses on problems at the interplay of learning and incentives in crowd-powered systems. Motivated by applications in community sensing, human computation, citizen science and crowdsourcing, we focus on particular technical challenges such as: How to optimally design incentives for learning / information gathering? How can one piece together a global picture from large numbers of noisy observations of a priori unknown quality?

We address these fundamental questions by building on state of the art results in machine learning, probabilistic modeling and game theory. Our research spans various application domains, including community sensing systems for earthquake detection and air-quality monitoring, rebalancing bike sharing systems by steering the behavior of the users, incentivizing users for privacy-tradeoff in information elicitation, teaching and training crowd workers, exploring social aspects of crowdsourcing, and learning optimal pricing policies for online crowdsourcing marketplaces.

Publications

2022
  • Incentive-Compatible Forecasting Competitions
  • J. Witkowski, R. Freeman, J. W. Vaughan, D. Pennock, A. Krause
  • In Management Science, 2022
  • [bibtex] [abstract] [pdf]
2018
  • Incentive-Compatible Forecasting Competitions
  • J. Witkowski, R. Freeman, J. W. Vaughan, D. Pennock, A. Krause
  • In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI’18), 2018
  • [bibtex] [abstract] [pdf]
2017
  • A Geometric Perspective on Minimal Peer Prediction
  • R. Frongillo, J. Witkowski
  • In ACM Transactions on Economics and Computation (TEAC), volume 5, 2017
  • [bibtex] [abstract] [pdf]
  • Learning and Incentives in Crowd-Powered Systems
  • A. Singla
  • PhD thesis, ETH Zurich, 2017
  • [bibtex] [abstract] [pdf]
  • Proper Proxy Scoring Rules
  • J. Witkowski, P. Atanasov, L. H. Ungar, A. Krause
  • In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI’17), 2017
  • forthcoming
  • [bibtex] [abstract] [pdf]
2016
  • Evaluating Task-Dependent Taxonomies for Navigation
  • Y. Sun, A. Singla, T. Yan, A. Krause, D. Fox
  • In AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2016
  • [bibtex] [abstract] [pdf]
  • Learning and Feature Selection under Budget Constraints in Crowdsourcing
  • B. Nushi, A. Singla, A. Krause, D. Kossmann
  • In AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2016
  • [bibtex] [abstract] [pdf]
  • Actively Learning Hemimetrics with Applications to Eliciting User Preferences
  • A. Singla, S. Tschiatschek, A. Krause
  • In Proc. International Conference on Machine Learning (ICML), 2016
  • [bibtex] [abstract] [pdf] [long]
  • Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization
  • A. Singla, S. Tschiatschek, A. Krause
  • In Proc. Conference on Artificial Intelligence (AAAI), 2016
  • [bibtex] [abstract] [pdf] [long]
2015
  • Crowd Access Path Optimization: Diversity Matters
  • B. Nushi, A. Singla, A. Gruenheid, E. Zamanian, A. Krause, D. Kossmann
  • In AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2015
  • [bibtex] [abstract] [pdf] [long]
  • Learning to Hire Teams
  • A. Singla, E. Horvitz, P. Kohli, A. Krause
  • In AAAI Conference on Human Computation and Crowdsourcing (HCOMP), short paper, 2015
  • [bibtex] [abstract] [pdf] [long]
  • Information Gathering in Networks via Active Exploration
  • A. Singla, E. Horvitz, P. Kohli, R. White, A. Krause
  • In International Joint Conference on Artificial Intelligence (IJCAI), 2015
  • [bibtex] [abstract] [pdf] [long]
  • Building Hierarchies of Concepts via Crowdsourcing
  • Y. Sun, A. Singla, D. Fox, A. Krause
  • In International Joint Conference on Artificial Intelligence (IJCAI), 2015
  • [bibtex] [abstract] [pdf] [long]
  • Robot Navigation in Dense Crowds: Statistical Models and Experimental Studies of Human Robot Cooperation
  • P. Trautman, J. Ma, R. Murray, A. Krause
  • In International Journal on Robotics Research (IJRR), 2015
  • [bibtex] [abstract] [doi]
  • Incentivizing Users for Balancing Bike Sharing Systems
  • A. Singla, M. Santoni, G. Bartok, P. Mukerji, M. Meenen, A. Krause
  • In Proc. Conference on Artificial Intelligence (AAAI), 2015
  • Selected for AAAI Open House
  • [bibtex] [abstract] [pdf]
2014
  • Mechanism Design for Crowdsourcing Markets with Heterogeneous Tasks
  • G. Goel, A. Nikzad, A. Singla
  • In AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2014
  • [bibtex] [abstract] [pdf] [long]
  • Quantifying Web-Search Privacy
  • A. Gervais, R. Shokri, A. Singla, S. Capkun, V. Lenders
  • In Proc. ACM Conference on Computer and Communications Security (CCS), 2014
  • [bibtex] [abstract] [pdf]
  • Community Sense and Response Systems: Your Phone as Quake Detector
  • M. Faulkner, R. Clayton, T. Heaton, K. M. Chandy, M. Kohler, J. Bunn, R. Guy, A. Liu, M. Olson, M. Cheng, A. Krause
  • In Communications of the ACM, volume 57, 2014
  • Cover Feature
  • [bibtex] [abstract] [doi]
  • Enhancing Personalization via Search Activity Attribution
  • A. Singla, R. W. White, A. Hassan, E. Horvitz
  • In Proc. Special Interest Group On Information Retrieval (SIGIR), 2014
  • [bibtex] [abstract] [pdf]
  • Near-Optimally Teaching the Crowd to Classify
  • A. Singla, I. Bogunovic, G. Bartók, A. Karbasi, A. Krause
  • In Proc. International Conference on Machine Learning (ICML), 2014
  • [bibtex] [abstract] [pdf] [long]
  • Stochastic Privacy
  • A. Singla, E. Horvitz, E. Kamar, R. W. White
  • In Proc. Conference on Artificial Intelligence (AAAI), 2014
  • [bibtex] [abstract] [pdf] [long]
  • Community sense and response systems
  • M. Faulkner
  • PhD thesis, California Institute of Technology, 2014
  • [bibtex] [abstract] [pdf]
  • Allocating Tasks to Workers with Matching Constraints: Truthful Mechanisms for Crowdsourcing Markets
  • G. Goel, A. Nikzad, A. Singla
  • In Proc. International World Wide Web Conference (WWW), 2014
  • [bibtex] [abstract] [pdf]
  • From Devices to People: Attribution of Search Activity in Multi-User Settings
  • R. W. White, A. Hassan, A. Singla, E. Horvitz
  • In Proc. International World Wide Web Conference (WWW), 2014
  • [bibtex] [abstract] [pdf]
2013
  • Incentives for Privacy Tradeoff in Community Sensing
  • A. Singla, A. Krause
  • In AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2013
  • [bibtex] [abstract] [pdf] [long]
  • Truthful Incentives in Crowdsourcing Tasks using Regret Minimization Mechanisms
  • A. Singla, A. Krause
  • In International World Wide Web Conference (WWW), 2013
  • [bibtex] [abstract] [pdf]
  • A Fresh Perspective: Learning to Sparsify for Detection in Massive Noisy Sensor Networks
  • M. Faulkner, A. Liu, A. Krause
  • In ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), 2013
  • Best Paper Award
  • [bibtex] [abstract] [pdf] [talk]
  • Robot Navigation in Dense Human Crowds: the Case for Cooperation
  • P. Trautman, J. Ma, R. Murray, A. Krause
  • In Proc. International Conference on Robotics and Automation (ICRA), 2013
  • Best Paper Award Finalist
  • [bibtex] [abstract] [pdf]
2012
  • Robot Navigation in Dense Crowds: Statistical Models and Experimental Studies of Human Robot Cooperation
  • P. Trautman
  • PhD thesis, California Institute of Technology, 2012
  • [bibtex] [abstract] [pdf]
2011
  • Community Seismic Network
  • R. Clayton, T. Heaton, M. Chandy, A. Krause, M. Kohler, J. Bunn, M. Olson, M. Faulkner, M. Cheng, L. Strand, R. Chandy, D. Obenshain, A. Liu, M. Aivazis, R. Guy
  • In Annals of Geophysics, volume 54, 2011
  • [bibtex] [abstract] [pdf]
  • The Next Big One: Detecting Earthquakes and other Rare Events from Community-based Sensors
  • M. Faulkner, M. Olson, R. Chandy, J. Krause, K. M. Chandy, A. Krause
  • In Proc. ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), 2011
  • [bibtex] [abstract] [pdf] [talk]
  • Randomized Sensing in Adversarial Environments
  • A. Krause, A. Roper, D. Golovin
  • In Proc. International Joint Conference on Artificial Intelligence (IJCAI), 2011
  • [bibtex] [abstract] [pdf] [talk]
  • Crowdclustering
  • R. Gomes, P. Welinder, A. Krause, P. Perona
  • In Proc. Neural Information Processing Systems (NeurIPS), 2011
  • [bibtex] [abstract] [pdf] [long]
2010
  • A Utility-Theoretic Approach to Privacy in Online Services
  • A. Krause, E. Horvitz
  • In Journal of Artificial Intelligence Research (JAIR), volume 39, 2010
  • [bibtex] [abstract] [pdf] [talk]
  • Unfreezing the Robot: Navigation in Dense, Interacting Crowds
  • P. Trautman, A. Krause
  • In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2010
  • [bibtex] [abstract] [pdf] [talk]
  • Online Distributed Sensor Selection
  • D. Golovin, M. Faulkner, A. Krause
  • In Proc. ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), 2010
  • [bibtex] [abstract] [pdf] [long] [talk]
2008
  • A Utility-Theoretic Approach to Privacy and Personalization
  • A. Krause, E. Horvitz
  • In Proc. 23rd Conference on Artificial Intelligence (AAAI), 2008
  • [bibtex] [abstract] [pdf] [talk]
  • Toward Community Sensing
  • A. Krause, E. Horvitz, A. Kansal, F. Zhao
  • In Proc. ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), 2008
  • [bibtex] [abstract] [pdf] [talk]
  • About
  • People
  • Publications
  • Blog
  • Teaching
  • Student Projects
  • Openings
  • Contact

Learning & Adaptive Systems Group | Institute for Machine Learning | © 2025 ETH Zurich