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|
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.
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.
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.