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 (May 2019)|
| nanjiang at illinois dot edu
|3322 Siebel Center|
Information-Theoretic Considerations in Batch Reinforcement Learning. [pdf]
(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, 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.