Luofeng Liao

Ph.D. in Operations Research

2025, Guangdong, China

Hi! My name is Luofeng Liao (廖烙锋 in Chinese). I am currently a Research Scientist in Meta working on ranking/recommendation algorithms. I completed my PhD in Operations Research at Columbia University, which I attended from 2021/09 to 2025/03. A bit on pronouncing my name: “Luo” (adding a ‘w’ sound in “law”), “Feng” (like “fung” in fung shui), “Liao” (rhymes with “meow”).

My research interests are machine learning methods for causal inference and econometrics, learning and inference in market design, and adversarial optimization.

Previously, I worked with my advisor Prof. Christian Kroer on statistical inference theory for equilibrium in auction markets, a theory that is useful for internet advertising business. Prior to Columbia, I received my bachelor’s degree in computer science in Fudan University in China and a master’s degree in statistics in University of Chicago. In UChicago, I used to work with Prof. Mladen Kolar and Prof. Zhaoran Wang. In Fudan I was advised by Prof. Ke Wei.

Papers

  1. NeurIPS2025
    The Bias-Variance Tradeoff in Data-Driven Optimization: A Local Misspecification Perspective
    Haixiang Lan, Luofeng Liao, Adam N. Elmachtoub, Christian Kroer, Henry Lam, and Haofeng Zhang
    2025
  2. Under Review, Management Science
    Online Fair Allocation with Best-of-Many-Worlds Guarantees
    Zongjun Yang, Luofeng Liao, Yuan Gao, and Christian Kroer
    2024
  3. Accepted, Management Science
    Statistical Inference and A/B Testing in Fisher Markets and Paced Auctions
    Luofeng Liao, and Christian Kroer
    2024
  4. ICLR2025
    Interference Among First-Price Pacing Equilibria: A Bias and Variance Analysis
    Luofeng Liao, Christian Kroer, Sergei Leonenkov, Okke Schrijvers, Liang Shi, Nicolas Stier-Moses, and Congshan Zhang
    2024
  5. ICML2024
    Bootstrapping Fisher Market Equilibrium and First-Price Pacing Equilibrium
    Luofeng Liao, and Christian Kroer
    2024
  6. AAAI2024
    Greedy-Based Online Fair Allocation with Adversarial Input: Enabling Best-of-Many-Worlds Guarantees
    Zongjun Yang, Luofeng Liao, and Christian Kroer
    2023
  7. ICML2023
    Statistical Inference and A/B Testing for First-Price Pacing Equilibria
    Luofeng Liao, and Christian Kroer
    2023
  8. ICLR2023
    Statistical Inference for Fisher Market Equilibrium
    Luofeng Liao, Yuan Gao, and Christian Kroer
    2022
  9. NeurIPS2022
    Nonstationary Dual Averaging and Online Fair Allocation
    Luofeng Liao, Yuan Gao, and Christian Kroer
    2022
  10. Machine Learning 2022
    Local AdaGrad-Type Algorithm for Stochastic Convex-Concave Minimax Problems
    Luofeng Liao, Li Shen, Jia Duan, Mladen Kolar, and Dacheng Tao
    Machine Learning - Springer 2022
  11. Journal of Machine Learning Research (JMLR) 2024
    Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning
    Luofeng Liao, Zuyue Fu, Zhuoran Yang, Yixin Wang, Mladen Kolar, and Zhaoran Wang
    Journal of Machine Learning Research 2024
  12. NeurIPS2020
    Provably efficient neural estimation of structural equation model: An adversarial approach
    Luofeng Liao, You-Lin Chen, Zhuoran Yang, Bo Dai, Zhaoran Wang, and Mladen Kolar
    2020