On Oracle-Efficient PAC RL with Rich Observations. [arXiv]
(NIPS-18, w/ spotlight talk) Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire.
Open Problem: The Dependence of Sample Complexity Lower Bounds on Planning Horizon. [pdf]
(COLT-18) Nan Jiang, Alekh Agarwal.
Hierarchical Imitation and Reinforcement Learning. [arXiv]
(ICML-18) Hoang M. Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, Hal Daumé III.
Markov Decision Processes with Continuous Side Information. [arXiv]
(ALT-18) Aditya Modi, Nan Jiang, Satinder Singh, Ambuj Tewari.
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.
On Structural Properties of MDPs that Bound Loss due to Shallow Planning. [pdf]
(IJCAI-16) Nan Jiang, Satinder Singh, Ambuj Tewari.
Improving Predictive State Representations via Gradient Descent. [pdf]
(AAAI-16) Nan Jiang, Alex Kulesza, Satinder Singh.
Low-Rank Spectral Learning with Weighted Loss Functions. [pdf]
(AISTATS-15) Alex Kulesza, Nan Jiang, Satinder Singh.
Spectral Learning of Predictive State Representations with Insufficient Statistics. [pdf]
(AAAI-15) Alex Kulesza, Nan Jiang, Satinder Singh.
A Theory of Model Selection in Reinforcement Learning. [pdf]
(2017) Nan Jiang.