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Publications

Conference Papers

1. Yifei Ma*, Balakrishnan (Murali) Narayanaswamy*, Haibin Lin, Hao Ding. Temporal-Contextual Recommendation in Real-Time. KDD 2020. (*Equal contribution authors) [Best Paper in Applied Data Science Track] [paper] [talk] [slides]
2. Tengyang Xie, Yifei Ma, Yu-Xiang Wang. Towards optimal off-policy evaluation for reinforcement learning with marginalized importance sampling. NeurIPS 2019. [paper]
3. Yifei Ma, Yu-Xiang Wang, Balakrishnan (Murali) Narayanaswamy. Imitation-Regularized Offline Learning. AISTATS 2019. [paper]
4. Hadi Salman, Elif Ayvali, Rangaprasad Arun Srivatsan, Yifei Ma, Nicolas Zevallos, Rashid Yasin, Long Wang, Nabil Simaan, Howie Choset. Trajectory-optimized sensing for active search of tissue abnormalities in robotic surgery. IEEE International Conference on Robotics and Automation (ICRA) 2018. [paper]
5. Yifei Ma, Roman Garnett, Jeff Schneider. Active Search for Sparse Signals with Region Sensing. AAAI 2017. [paper] [poster] [arxiv]
6. Yifei Ma, Tzu-Kuo Huang, Jeff Schneider. Active Search and Bandits on Graphs Using Sigma-Optimality. UAI 2015. [paper] [codes] [spotlight] [poster]
7. Yifei Ma, Dougal J. Sutherland, Roman Garnett, Jeff Schneider. Active Pointillistic Pattern Search. AISTATS 2015. Two Shared Lead Authors. [paper] [supp] [spotlight] [poster]
8. Yifei Ma, Roman Garnett, Jeff Schneider. Active Area Search via Bayesian Quadrature. AISTATS 2014. [paper]
9. Yifei Ma, Roman Garnett, Jeff Schneider. Sigma-Optimality for Active Learning on Gaussian Random Fields. NIPS 2013. [paper] [codes] [poster]
10. Guangyu Xia, Tongbo Huang, Yifei Ma, Roger B. Dannenberg, Christos Faloutsos. MidiFind: Similarity Search and Popularity Mining in Large MIDI Databases. CMMR 2013: 259-276.
11. Yifei Ma, Li Li, Xiaolin Huang, Shuning Wang, Robust Support Vector Machine Using Least Median Loss Penalty, Proceedings of the 18th IFAC World Congress, Volume 18, Part 1, 2011. [paper]
    

Reports

1. Yifei Ma. Thesis: Active search with Complex Actions and Rewards [doc] [slides]
2. Yifei Ma. Data Analysis Project: Sigma-Optimality for Active Learning on Gaussian Random Fields. [paper]

Preprints

1. Yifei Ma, Ge Liu, Anoop Deoras. Recurrent Intensity Model for User Recommendation and Online Matching. ICML 2021 Time Series Workshop. [paper] [poster]
2. Haibin Lin, Hang Zhang, Yifei Ma, Tong He, Zhi Zhang, Sheng Zha, Mu Li. Dynamic mini-batch SGD for elastic distributed training: learning in the limbo of resources. arXiv:1904.12043. 2019. [paper]
3. Tengyang Xie, Yu-Xiang Wang, Yifei Ma. Marginalized Off-Policy Evaluation for Reinforcement Learning. NIPS 2018 Workshop on Causal Learning. [paper] [workshop]
4. Yifei Ma, Balakrishnan (Murali) Narayanaswamy. Hierarchical Temporal-Contextual Recommenders. NIPS 2018 Workshop on Modeling and Decision-Making in Spatiotemporal Domains. [paper] [workshop]
5. Yifei Ma, Roman Garnett, Jeff Schneider, Andrew Gordon Wilson. Fast Bayesian Optimization via Conjugate Sampling. NIPS 2016 Workshop on Practical Bayesian Nonparametric. [paper] [workshop] [poster]
6. Yifei Ma, Roman Garnett, Jeff Schneider. Active Search for Sparse Signals with Region Sensing. ICML 2016 Workshop on Data-Efficient Machine Learning. [paper] [workshop]
7. Yifei Ma, Dougal J. Sutherland, Roman Garnett, Jeff Schneider. Active Pointillistic Pattern Search. NIPS 2014 Workshop on Bayesian Optimization.
8. Yifei Ma, Roman Garnett, Jeff Schneider. Submodularity in Batch Active Learning and Survey Problems on Gaussian Random Fields. NIPS 2012 Workshop on DISCML.

Experience

Senior Applied Scientist at Amazon.Com. 7/2017-now (East Palo Alto, CA)

• Founding science lead of Amazon Personalize: a fully-managed service that enables developers to build applications for real-time personalized recommendations with no ML expertise required.
• Contributor in MXNet library for deep learning, GluonNLP, Deep Graphical Library.

Quantitative Researcher Intern at Citadel LLC. 5/2014-8/2014 (Chicago, IL)

• Feature design and analysis for market value prediction.
• Bayesian methods for low-frequency predictions.

Education

Ph.D. Student in Machine Learning, Carnegie Mellon University, 8/2011-5/2017.

• Advised by Jeff Schneider.

B.S. in Automation, Dual B.S. in Mathematics, Tsinghua University, 8/2007-7/2011.

• 1st GPA in Automation by the time of graduate school application.
• Totally 11 students dual majored in Mathematics in a class of 3000+ students.

Exchange Study (Credits Transferred), Georgia Inst. of Technology, 8/2009-12/2009.

• A rare opportunity given to only 6 students university-wide.

Honors and Awards

• 2007-2010, 1st Level Academic Scholarship every year in Tsinghua University;
• 10/2009, 31/700 in IEEEXtreme 24-Hour Programming Competition (team leader);
• 10/2006, 1st prize in National High School Physics Competition;
• 03/2006, Qualified for the American Invitational Mathematics Examination (no further award for foreigners);
• 10/2004, 1st prize in National High School Math Competition (over 2 years younger than most others).

Volunteer Experience

• Joined student volunteer group for over 10 years;
• Served as a regular campus guide for visitors in Tsinghua University;
• Volunteered for the 2008 Beijing Olympic Games (in medal ceremonies as a winning national flag raiser).

Student Activities and Hobbies

• Editor for Tsinghua Student Psychology Club Journal;
• Member of Campus Tour Guide Association;
• Member of Tsinghua Honor Guard (to display and escort the national flag on ceremonial occasions);
• Classical Chinese Philosophy like the Analects (Confucius) and Tao Te Qing (Lao-tzu);
• Piano, swimming, running, and wall climbing.