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Travelled to:
1 × Finland
1 × France
1 × Germany
1 × United Kingdom
2 × Canada
2 × China
7 × USA
Collaborated with:
Y.Engel T.A.Mann A.Tamar D.D.Castro J.Y.Yu M.Harel J.N.Tsitsiklis Y.Mansour H.Xu D.Precup R.Meir R.Y.Rubinstein A.Hallak O.Richman O.Maillard E.Delage A.Gopalan D.J.Mankowitz Y.Chen C.Caramanis O.Avner O.Shamir O.Mebel D.Vainsencher O.Dekel J.Frank P.W.Keller D.Peleg E.Even-Dar Y.Gat F.Schnitzler R.El-Yaniv K.Crammer I.Menache A.Hoze U.Klein D.Simester P.Sun
Talks about:
learn (9) process (6) reinforc (5) function (4) varianc (4) polici (4) bandit (4) dynam (4) approxim (3) robust (3)

Person: Shie Mannor

DBLP DBLP: Mannor:Shie

Contributed to:

ICML 20152015
ICML c1 20142014
ICML c2 20142014
ICML c3 20132013
KDD 20132013
ICML 20122012
ICML 20112011
ICML 20092009
ICML 20082008
ICML 20072007
ICML 20062006
ICML 20052005
ICML 20042004
ICML 20032003
ICML 20012001

Wrote 30 papers:

ICML-2015-HallakSMM #learning #modelling
Off-policy Model-based Learning under Unknown Factored Dynamics (AH, FS, TAM, SM), pp. 711–719.
ICML-2015-RichmanM #classification #constraints
Dynamic Sensing: Better Classification under Acquisition Constraints (OR, SM), pp. 267–275.
ICML-c1-2014-GopalanMM #online #problem
Thompson Sampling for Complex Online Problems (AG, SM, YM), pp. 100–108.
Latent Bandits (OAM, SM), pp. 136–144.
ICML-c1-2014-MannM #approximate #policy #scalability
Scaling Up Approximate Value Iteration with Options: Better Policies with Fewer Iterations (TAM, SM), pp. 127–135.
ICML-c2-2014-HarelMEC #concept #detection
Concept Drift Detection Through Resampling (MH, SM, REY, KC), pp. 1009–1017.
Time-Regularized Interrupting Options (TRIO) (TAM, DJM, SM), pp. 1350–1358.
ICML-c2-2014-TamarMX #approximate #robust #scalability #using
Scaling Up Robust MDPs using Function Approximation (AT, SM, HX), pp. 181–189.
ICML-c3-2013-ChenCM #robust
Robust Sparse Regression under Adversarial Corruption (YC, CC, SM), pp. 774–782.
ICML-c3-2013-TamarCM #difference
Temporal Difference Methods for the Variance of the Reward To Go (AT, DDC, SM), pp. 495–503.
KDD-2013-HallakCM #markov #process
Model selection in markovian processes (AH, DDC, SM), pp. 374–382.
ICML-2012-AvnerMS #multi
Decoupling Exploration and Exploitation in Multi-Armed Bandits (OA, SM, OS), p. 145.
ICML-2012-CastroTM #policy
Policy Gradients with Variance Related Risk Criteria (DDC, AT, SM), p. 215.
ICML-2012-MannorMX #nondeterminism #robust
Lightning Does Not Strike Twice: Robust MDPs with Coupled Uncertainty (SM, OM, HX), p. 62.
ICML-2011-HarelM #learning #multi
Learning from Multiple Outlooks (MH, SM), pp. 401–408.
ICML-2011-MannorT #markov #optimisation #process
Mean-Variance Optimization in Markov Decision Processes (SM, JNT), pp. 177–184.
ICML-2011-VainsencherDM #estimation #online
Bundle Selling by Online Estimation of Valuation Functions (DV, OD, SM), pp. 1137–1144.
Unimodal Bandits (JYY, SM), pp. 41–48.
ICML-2009-YuM #problem
Piecewise-stationary bandit problems with side observations (JYY, SM), pp. 1177–1184.
ICML-2008-FrankMP #learning
Reinforcement learning in the presence of rare events (JF, SM, DP), pp. 336–343.
ICML-2007-DelageM #markov #nondeterminism #optimisation #performance #process
Percentile optimization in uncertain Markov decision processes with application to efficient exploration (ED, SM), pp. 225–232.
ICML-2006-KellerMP #approximate #automation #learning #programming
Automatic basis function construction for approximate dynamic programming and reinforcement learning (PWK, SM, DP), pp. 449–456.
ICML-2005-EngelMM #learning #process
Reinforcement learning with Gaussian processes (YE, SM, RM), pp. 201–208.
ICML-2005-MannorPR #classification
The cross entropy method for classification (SM, DP, RYR), pp. 561–568.
ICML-2004-MannorMHK #abstraction #clustering #learning
Dynamic abstraction in reinforcement learning via clustering (SM, IM, AH, UK).
ICML-2004-MannorSST #bias #estimation
Bias and variance in value function estimation (SM, DS, PS, JNT).
ICML-2003-EngelMM #approach #difference #learning #process
Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning (YE, SM, RM), pp. 154–161.
ICML-2003-Even-DarMM #learning
Action Elimination and Stopping Conditions for Reinforcement Learning (EED, SM, YM), pp. 162–169.
ICML-2003-MannorRG #performance #policy
The Cross Entropy Method for Fast Policy Search (SM, RYR, YG), pp. 512–519.
ICML-2001-EngelM #embedded #learning #markov #process
Learning Embedded Maps of Markov Processes (YE, SM), pp. 138–145.

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.