Travelled to:1 × Australia
1 × China
1 × Finland
1 × Israel
1 × United Kingdom
2 × Canada
3 × USA
Collaborated with:C.Painter-Wakefield G.Taylor M.G.Lagoudakis J.Pazis A.I.Eliazar L.Li M.L.Littman C.Guestrin M.Petrik S.Zilberstein L.Lim M.Wang S.Padmanabhan J.S.Vitter
Talks about:learn (6) reinforc (5) function (4) approxim (4) valu (4) select (3) linear (3) featur (3) markov (2) model (2)
Person: Ronald Parr
DBLP: Parr:Ronald
Contributed to:
201220112010200920082007200420032002
2002Wrote 10 papers:
- ICML-2012-Painter-WakefieldP #algorithm #learning
- Greedy Algorithms for Sparse Reinforcement Learning (CPW, RP), p. 114.
- ICML-2011-PazisP #scalability #set
- Generalized Value Functions for Large Action Sets (JP, RP), pp. 1185–1192.
- ICML-2010-PetrikTPZ #approximate #feature model #linear #markov #process #source code #using
- Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes (MP, GT, RP, SZ), pp. 871–878.
- ICML-2009-TaylorP #approximate #kernel #learning
- Kernelized value function approximation for reinforcement learning (GT, RP), pp. 1017–1024.
- 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-2004-EliazarP #learning #mobile #modelling #probability
- Learning probabilistic motion models for mobile robots (AIE, RP).
- ICML-2003-LagoudakisP #classification #learning
- Reinforcement Learning as Classification: Leveraging Modern Classifiers (MGL, RP), pp. 424–431.
- ICML-2002-GuestrinLP #coordination #learning
- Coordinated Reinforcement Learning (CG, MGL, RP), pp. 227–234.
- VLDB-2002-LimWPVP #estimation #markov #named #online #self #xml
- XPathLearner: An On-line Self-Tuning Markov Histogram for XML Path Selectivity Estimation (LL, MW, SP, JSV, RP), pp. 442–453.