Yang Liu


Yang Liu
Postdoctoral fellow
SEAS, CRCS, Harvard University
Office: MD-110

About me

    Hello. I'm currently a postdoctoral fellow at Harvard University. I am very fortunate to be hosted by Professor Yiling Chen. I also affiliate with the Center for Research on Computation and Society. My research concerns the interaction between artificial intelligence (e.g., machine learning) with our people and society. Examples of topics that I enjoy researching on: machine learning aided incentive design (for information elicitation), crowdsourcing, fairness in AI/ML, security/privacy for human data.


  • [2017.11] We built a reinforcement learning framework for eliciting high quality information (reinforcement peer prediction). Preliminary version of this work is going to appear at Machine Learning in the Presence of Strategic Behavior at NIPS'17.
  • [2017.11] Our work (with CJ Ho) "Incentivizing High Quality User Contributions: New Arm Generation in Bandit Learning" is going to appear at AAAI'18.
  • [2017.07] Attended BayesianCrowd and presented our results on machine learning aided peer prediction.
  • [2017.06] Our paper "Calibrated Fairness in Bandits" is going to appear at FATML'17 (oral presentation, 8/48)!
  • [2017.06] Maintaining fairness in a decision making process: my paper "Fair Optimal Stopping Policy for Sequential Matching" is going to appear at UAI'17.
  • [2017.05] New papers to appear: Machine Learning aided Peer Prediction (for data elicitation) at EC'17 (with Yiling), and Crowd Learning: Improving Online Decision Making Using Crowdsourced Data at IJCAI'17 (with Mingyan).

Selected / Recent Publications

<Click for the Full List>

Machine learning aided incentive design [Overview]

Surrogate Scoring Rules and a Dominant Truth Serum for Information Elicitation.[pdf coming soon]
Yang Liu and Yiling Chen, in preparation, 2017.

Machine Learning aided Peer Prediction.[Video] [Slides]
Yang Liu and Yiling Chen, ACM EC 2017, Cambridge, United States.

A Bandit Framework for Strategic Regression.[Video] [Poster]
Yang Liu and Yiling Chen, NIPS 2016, Barcelona, Spain.

AI/ML for social good (fairness and security) [Overview]

Calibrated Fairness in Bandits (Fair Experimentation).[Slides coming soon]
Yang Liu, Goran Radanovic, Christos Dimitrakakis, Debmalya Mandal and David Parkes
FATML 2017, Halifax, Canada; also to appear at CODE@MIT 2017, Cambridge, United States.

Fair Optimal Stopping Policy for Sequential Matching.[Poster]
Yang Liu, UAI 2017, Sydney, Australia.

Cloudy with a Chance of Breach: Forecasting Cyber Security Incidents. [Slides]
Yang Liu, Armin Sarabi, Jing Zhang, Parinaz Ardabili, Manish Karir, Michael Bailey and Mingyan Liu
USENIX Security 2015, Washington, D.C., United States.

  • More details: Project webpage. You will also find a sample dataset to play with.
  • This work is featured in a WSJ Article.
  • QuadMetrics,Inc (acquired by FICO) , a start-up co-founded by Mingyan, is using the technique documented in the paper.
  • Patent: Rating Network Maliciousness and Comparing Network Maliciousness Through Similarity Analyses, No. 62/026,349.

Learning the wisdom of crowd [Overview]

An Online Learning Approach to Improving the Quality of Crowd-Sourcing.[Slides]
Yang Liu and Mingyan Liu, ACM SIGMETRICS 2015, Portland, United States.

Crowd Learning: Improving Online Decision Making Using Crowdsourced Data.[Slides]
Yang Liu and Mingyan Liu, IJCAI 2017, Melbourne, Australia.

[Tutorial] Bandit in Crowdsourcing.[Slides]
Yang Liu, CMO-BIRS Workshop: Models and Algorithms for Crowds and Networks 2016, Oxaca, Mexico.