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.


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

CV (Mar 2018).
Research statement (Nov 2016).

nanjiang at illinois dot edu

Selected Publications
(click to expand)

Contextual Decision Processes with Low Bellman Rank are PAC-Learnable. [ICML version, arXiv, 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.