2100 Uniersity Avenue
East Palo Alto, CA 94040

Email: Please find in my paper links or use the discussions at the bottom of the page.

I am a Machine Learning Scientist in Amazon, working on general-purpose recommender systems. This is a precious and mostly harmless environment to practice statistical skills such as bias-variance trade-offs, optimal designs of experiments, causal reasoning, dynamic system modeling, etc.

I was a PhD graduate from Machine Learning Department, Carnegie Mellon University. I work on active search, which is a set of algorithms that help users find all positive examples in an unknown environment by selecting queries and learning from their label feedback. Usually, the labels are costly. My research aims to use active search for complex tasks where the queries and rewards are not necessarily directly related.

My work enabled many new applications in information discovery, social science, and robotics. While solving these problems, I also built theoretical insights using spectral graph theories, regret analysis, combinatorial optimization, information theory, and more.

Concepts in interactive machine learning can help/has helped us better understand passive machine learning. Conversely, successes from passive learning can inspire active systems to handle more sophisticated applications. My hope is to bring the best of both worlds to build new applications.


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]


  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]


  1. 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]
  2. Tengyang Xie, Yu-Xiang Wang, Yifei Ma. Marginalized Off-Policy Evaluation for Reinforcement Learning. NIPS 2018 Workshop on Causal Learning. [paper] [workshop]
  3. Yifei Ma, Balakrishnan (Murali) Narayanaswamy. Hierarchical Temporal-Contextual Recommenders. NIPS 2018 Workshop on Modeling and Decision-Making in Spatiotemporal Domains. [paper] [workshop]
  4. 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]
  5. Yifei Ma, Roman Garnett, Jeff Schneider. Active Search for Sparse Signals with Region Sensing. ICML 2016 Workshop on Data-Efficient Machine Learning. [paper] [workshop]
  6. Yifei Ma, Dougal J. Sutherland, Roman Garnett, Jeff Schneider. Active Pointillistic Pattern Search. NIPS 2014 Workshop on Bayesian Optimization.
  7. Yifei Ma, Roman Garnett, Jeff Schneider. Submodularity in Batch Active Learning and Survey Problems on Gaussian Random Fields. NIPS 2012 Workshop on DISCML.


Citadel LLC. Quantitative Researcher Intern. 5/2014-8/2014 at Chicago.


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

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

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

Honors and Awards

Volunteer Experience

Student Activities and Hobbies

Photo of me.