Research AreasMachine Learning, Tree Ensembles, Bayesian Statistics, Empirical Asset Pricing.
* indicates alphabetical order.
- Jingyu He, Saar Yalov, Jared Murray and P. Richard Hahn (2019). Stochastic tree ensembles for regularized supervised learning. Job Market Paper.
- Guanhao Feng and Jingyu He* (2018). Factor Investing: Hierarchical Ensemble Learning. Submitted.
- Christopher Glynn, Jingyu He*, Nicholas Polson and Jianeng Xu (2019). Bayesian Inference for Polya Inverse Gamma Models. Submitted.
- Guanhao Feng, Jingyu He* and Nicholas G. Polson (2018). Deep Learning for Predicting Asset Returns. Working paper.
- Jingyu He, Saar Yalov and P. Richard Hahn (2019). XBART: Accelerated Bayesian Additive Regression Trees. The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS). slides, poster, package: XBART.
- P. Richard Hahn, Jingyu He and Hedibert Lopes (2019). Efficient sampling for Gaussian linear regression with arbitrary priors. Journal of Computational and Graphical Statistics 28.1 (2019): 142-154. R package : bayeslm, Demo Script.
- P. Richard Hahn, Carlos M. Carvalho, Jingyu He and David Puelz (2018). Regularization and confounding in linear regression for treatment effect estimation. Bayesian Analysis 13 (1), 163-182.
- P. Richard Hahn, Jingyu He and Hedibert Lopes (2018). Bayesian factor model shrinkage for linear IV regression with many instruments. Journal of Business and Economic Statistics 36 (2), 278-287.
Abstract: This paper introduces a novel stochastic tree ensemble method for regression and clas- sification (i.e. supervised machine learning). By combining regularization and stochastic search strategies from Bayesian modeling with computationally efficient techniques for recursive parti- tioning, the new method attains state-of-the-art performance: in many settings it is both faster and more accurate than the widely-used XGBoost algorithm.
Working in Progress
- Hierarchical Mixture Models with Elliptical Slice Sampling, with Sanjog Misra and Peter Rossi.
- Deep Learning Particle Filtering, with Nicholas Polson and Yuexi Wang.
RefereeJournal of Econometrics, Jounral of Empirical Finance.
Invited Seminars10/2019 Arizona State University, Department of Statistics.
Conference Talks05/2020 ICSA 2020 Applied Statistics Symposium, Houston.
07/2019 China International Conference in Finance, Guangzhou.
06/2019 Asia Meeting of the Econometric Society (2019 AMES), Xiamen.
06/2019 NBER-NSF Seminar on Bayesian Inference in Econometrics and Statistics, Brown University.
05/2019 China R Conference, Renmin University.
05/2019 R in Finance, University of Illinois at Chicago.
04/2019 International Conference on FinTech, Shanghai Jiao Tong University.
03/2019 Econometrics and Statistics Lunch Seminar, University of Chicago Booth School of Business.
07/2017 Joint Statistical Meetings, Baltimore.
06/2016 International Society of Bayesian Analysis World Meeting, Sardinia, Italy.
04/2016 NBER-NSF Seminar on Bayesian Inference in Econometrics and Statistics, Wharton.