Hi, this is the home page of Nan Jiang (姜楠). I am a machine learning researcher. My core research area is reinforcement learning (RL). I care about the sample efficiency of RL, and use ideas from statistical learning theory to analyze and develop RL algorithms. I am also interested in related areas such as online learning and dynamical system modeling.

Prospective students: please read this note.


2018 – Now   Assistant Professor, CS @ UIUC
2017 – 2018   Postdoc Researcher, MSR NYC
2011 – 2017   PhD, CSE @ UMich

CV (Oct 2019)  
nanjiang at illinois dot edu
3322 Siebel Center  

Selected Publications
(click to expand)

Minimax Weight and Q-Function Learning for Off-Policy Evaluation. [arXiv, simplified note]
(preprint) Masatoshi Uehara, Nan Jiang.
Learning importance weights and value functions from each other, with connections to many old and new algorithms in RL.

Information-Theoretic Considerations in Batch Reinforcement Learning. [pdf, poster, MSR talk, Simons talk]
(ICML-19) Jinglin Chen, Nan Jiang.
Revisiting some fundamental aspects of value-based RL.

Contextual Decision Processes with Low Bellman Rank are PAC-Learnable. [ICML version, arXiv, errata, poster, talk video]
(ICML-17) Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire.
A new and general theory of exploration in RL with function approximation.

Doubly Robust Off-policy Value Evaluation for Reinforcement Learning. [pdf, poster]
(ICML-16) Nan Jiang, Lihong Li.
Simple and effective improvement of importance sampling via control variates.

The Dependence of Effective Planning Horizon on Model Accuracy. [pdf, errata, poster, talk video]
(AAMAS-15, best paper award) Nan Jiang, Alex Kulesza, Satinder Singh, Richard Lewis.
Using a smaller discount factor than defined can be viewed as regularization.

Low-Rank Spectral Learning with Weighted Loss Functions. [pdf]
(AISTATS-15) Alex Kulesza, Nan Jiang, Satinder Singh.
Approximation guarantees for low-rank learning of PSRs.