Luofeng Liao

Department of Industrial Engineering and Operations Research, Columbia University

2021 @ Columbia

Hi! My name is Luofeng Liao (廖烙锋 in Chinese) and I am a second-year phd student in the IEOR Department at Columbia.

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

Currently, I’m working with 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. preprint
    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
  2. preprint
    Bootstrapping Fisher Market Equilibrium and First-Price Pacing Equilibrium
    Luofeng Liao, and Christian Kroer
    2024
  3. AAAI2024
    Greedy-Based Online Fair Allocation with Adversarial Input: Enabling Best-of-Many-Worlds Guarantees
    Zongjun Yang, Luofeng Liao, and Christian Kroer
    2023
  4. ICML2023
    Statistical Inference and A/B Testing for First-Price Pacing Equilibria
    Luofeng Liao, and Christian Kroer
    2023
  5. ICLR2023
    Statistical Inference for Fisher Market Equilibrium
    Luofeng Liao, Yuan Gao, and Christian Kroer
    2022
  6. NeurIPS2022
    Nonstationary Dual Averaging and Online Fair Allocation
    Luofeng Liao, Yuan Gao, and Christian Kroer
    2022
  7. MLJ
    Local AdaGrad-Type Algorithm for Stochastic Convex-Concave Minimax Problems
    Luofeng Liao, Li Shen, Jia Duan, Mladen Kolar, and Dacheng Tao
    Machine Learning Journal 2022
  8. preprint
    Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning
    Luofeng Liao, Zuyue Fu, Zhuoran Yang, Yixin Wang, Mladen Kolar, and Zhaoran Wang
    2021
  9. 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