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Travelled to:
1 × China
1 × Finland
2 × Canada
4 × USA
Collaborated with:
M.L.Littman J.Langford E.Brunskill ∅ R.Parr C.Painter-Wakefield W.Chu M.Dudík C.Diuk B.R.Leffler T.J.Walsh M.Zinkevich A.Thomas B.L.Tseng G.Taylor A.L.Strehl E.Wiewiora A.Agarwal D.Hsu S.Kale R.E.Schapire T.Moon C.Liao Z.Zheng Y.Chang
Talks about:
learn (9) reinforc (4) function (3) approxim (3) linear (3) featur (3) select (2) onlin (2) model (2) valu (2)

Person: Lihong Li

DBLP DBLP: Li:Lihong

Contributed to:

ICML c2 20142014
ICML 20112011
KDD 20112011
CIKM 20102010
ICML 20092009
ICML 20082008
ICML 20072007
ICML 20062006

Wrote 11 papers:

ICML-c2-2014-AgarwalHKLLS #algorithm #performance
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits (AA, DH, SK, JL, LL, RES), pp. 1638–1646.
ICML-c2-2014-BrunskillL #learning
PAC-inspired Option Discovery in Lifelong Reinforcement Learning (EB, LL), pp. 316–324.
ICML-2011-DudikLL #evaluation #learning #policy #robust
Doubly Robust Policy Evaluation and Learning (MD, JL, LL), pp. 1097–1104.
KDD-2011-ChuZLTT #data type #learning #online
Unbiased online active learning in data streams (WC, MZ, LL, AT, BLT), pp. 195–203.
CIKM-2010-MoonLCLZC #feedback #learning #online #ranking #realtime #using
Online learning for recency search ranking using real-time user feedback (TM, LL, WC, CL, ZZ, YC), pp. 1501–1504.
ICML-2009-DiukLL #adaptation #feature model #learning #problem
The adaptive k-meteorologists problem and its application to structure learning and feature selection in reinforcement learning (CD, LL, BRL), pp. 249–256.
ICML-2008-Li #approximate #comparison #difference #linear #worst-case
A worst-case comparison between temporal difference and residual gradient with linear function approximation (LL), pp. 560–567.
ICML-2008-LiLW #framework #learning #self #what
Knows what it knows: a framework for self-aware learning (LL, MLL, TJW), pp. 568–575.
ICML-2008-ParrLTPL #analysis #approximate #feature model #learning #linear #modelling
An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning (RP, LL, GT, CPW, MLL), pp. 752–759.
ICML-2007-ParrPLL #approximate #generative
Analyzing feature generation for value-function approximation (RP, CPW, LL, MLL), pp. 737–744.
ICML-2006-StrehlLWLL #learning
PAC model-free reinforcement learning (ALS, LL, EW, JL, MLL), pp. 881–888.
Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
Hosted as a part of SLEBOK on GitHub.