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Solving stochastic optimization problems under partial observability, where one needs to adaptively make decisions with uncertain outcomes, is a fundamental but notoriously difficult challenge.
Linear programming under uncertainty
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The optimal control of partially observable Markov decision processes over a finite horizon
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Performance bounds on the splitting algorithm for binary testing
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Accelerated greedy algorithms for maximizing submodular set functions
Michel Minoux · 1978
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An analysis of approximations for maximizing submodular set functions - I
George L. Nemhauser, Laurence A. Wolsey, and Marshall L. Fisher · 1978
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A dynamic allocation index for the discounted multiarmed bandit problem
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An analysis of the greedy algorithm for the submodular set covering problem
Laurence A. Wolsey · 1982
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The complexity of Markov decision processses
C. H. Papadimitriou and J. N. Tsitsiklis · 1987
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Esther M. Arkin, Henk Meijer, Joseph S. B. Mitchell, David Rappaport, and Steven S. Skiena · 1993
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David A. Cohn, Zoubin Gharamani, and Michael I. Jordan · 1996
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A sub-constant error–probability low–degree test, and a sub–constant error-probability PCP characterization of NP
Ran Raz and Shmuel Safra · 1997
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A threshold of ln n for approximating set cover
Uriel Feige · 1998
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Efficient global optimization of expensive black-box functions
Donald R. Jones, Matthias Schonlau, and William J. Welch · 1998
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The computational complexity of probabilistic planning
M. Littman, J. Goldsmith, and M. Mundhenk · 1998
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Employing EM and pool-based active learning for text classification
Andrew McCallum and Kamal Nigam · 1998
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On an optimal split tree problem
S. Rao Kosaraju, Teresa M. Przytycka, and Ryan S. Borgstrom · 1999
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Maximum entropy sampling and optimal Bayesian experimental design
P. Sebastiani and H. P. Wynn · 2000
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Maximizing the spread of influence through a social network
David Kempe, Jon Kleinberg, and Éva Tardos · 2003
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Combinatorial optimization : polyhedra and efficiency
Alexander Schrijver · 2003
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Analysis of a greedy active learning strategy
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Model-driven data acquisition in sensor networks
A. Deshpande, C. Guestrin, S. Madden, J. Hellerstein, and W. Hong · 2004
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Uriel Feige, László Lovász, and Prasad Tetali · 2004
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B. Dean, M.X. Goemans, and J. Vondrák · 2005
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Submodular functions and optimization , volume 58
Satoru Fujishige · 2005
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The online set cover problem
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A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning
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The design of competitive online algorithms via a primal–dual approach
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Stochastic depletion problems: Effective myopic policies for a class of dynamic optimization problems
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Multi-armed bandits with metric switching costs
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Michel X. Goemans and Jan Vondrák · 2006
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Revisiting the greedy approach to submodular set function maximization
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