Empirical Likelihood with Generative AI
Nonparametric Bayesian empirical likelihood methods for moment-restriction models, using AI-generated auxiliary data as indirect regularization.
PhD student in Econometrics and Statistics
Jiguang Li
My research develops Bayesian methods for robust inference and adaptive decision-making, especially in settings with latent structure, sequential data collection, or partial model specification. I draw on nonparametric Bayes, reinforcement learning, and statistical computing to study problems in econometrics and psychometrics.
I am a PhD student in Econometrics and Statistics at the University of Chicago Booth School of Business, advised by Veronika Ročková. Previously, I worked as a full-time research professional at the Center for Applied Artificial Intelligence under the supervision of Sendhil Mullainathan.
Before Booth, I received an M.S. in Statistics from Yale University and a B.A. in Mathematics from Middlebury College.
Publications and Preprints
Dynamic Treatment on Networks
Offline reinforcement-learning methods for dynamic treatment allocation under network interference.
Robust Bayesian Optimization via Tempered Posteriors
Tempered posteriors for robust Bayesian optimization, with online calibration and regret guarantees.
Deep Computerized Adaptive Testing
Deep Q-learning for nonmyopic computerized adaptive testing with multidimensional latent traits.
Sparse Bayesian Multidimensional Item Response Theory
A scalable nonparametric Bayesian framework for learning sparse latent structure in multidimensional item response data.
Other Writing
Latent Variable Models Notes
Based on David Blei's Build, Compute, Critique, Repeat: Data Analysis with Latent Variable Models.
Mixing Markov Chains
An expository note on fastest-mixing Markov chains on star graphs.
Undergraduate Thesis in Algebraic Combinatorics
The Chevalley-Warning Theorem: Its Proofs, Generalizations, and Applications.
On Exploration and Exploitation
RL case study.