Research Interests

• Causal inference
• Multiple hypothesis testing
• Reinforcement learning / Bandits

Papers & Preprints

• Towards optimal variance reduction in online controlled experiments
  Ying Jin and Shan Ba
  Extended abstract accepted to Conference on Digital Experimentation (CODE@MIT), 2021

• Contemporary symbolic regression methods and their relative performance
  William La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabricio Olivetti de Franca, Marco Virgolin, Ying Jin, Michael Kommenda and Jason H. Moore
  Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks , 2021

• One estimator, many estimands: fine-grained quantification of uncertainty using conditional inference
  Ying Jin and Dominik Rothenhäusler. Preprint, 2021

• Is pessimism provably efficient for offline RL?
  Ying Jin*, Zhuoran Yang and Zhaoran Wang
  International Conference on Machine Learning (ICML), 2021

(* equal contribution)

Talks

• One estimator, many estimands: fine-grained quantification of uncertainty using conditional inference. Joint Statistical Meetings (speed presentation), August 2021. [video] [slides]

• Is pessimism provably efficient in offline RL? Online RL Theory Seminars, April 2021. [video] [slides]

Undergraduate Work

• Computational-statistical tradeoffs in inferring combinatorial structures of Ising model
  Ying Jin, Zhaoran Wang and Junwei Lu
  International Conference on Machine Learning (ICML), 2020

• Bayesian symbolic regression
  Ying Jin, Weilin Fu, Jian Kang, Jiadong Guo and Jian Guo
  Proceedings of the 9th International Workshop on Statistical Relational Artificial Intelligence, 2020

• Asymptotics for risk-parameter estimation in zero-drift GJR-GARCH models
  Ying Jin and Dong Li. Undergraduate Thesis.