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MP
2002
93views more  MP 2002»
13 years 5 months ago
Conditioning of convex piecewise linear stochastic programs
In this paper we consider stochastic programming problems where the objective function is given as an expected value of a convex piecewise linear random function. With an optimal s...
Alexander Shapiro, Tito Homem-de-Mello, Joocheol K...
MP
2006
107views more  MP 2006»
13 years 5 months ago
Convergence theory for nonconvex stochastic programming with an application to mixed logit
Monte Carlo methods have been used extensively in the area of stochastic programming. As with other methods that involve a level of uncertainty, theoretical properties are required...
Fabian Bastin, Cinzia Cirillo, Philippe L. Toint
NIPS
2001
13 years 6 months ago
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning
Policy gradient methods for reinforcement learning avoid some of the undesirable properties of the value function approaches, such as policy degradation (Baxter and Bartlett, 2001...
Evan Greensmith, Peter L. Bartlett, Jonathan Baxte...
UAI
2004
13 years 6 months ago
Bidding under Uncertainty: Theory and Experiments
This paper describes a study of agent bidding strategies, assuming combinatorial valuations for complementary and substitutable goods, in three auction environments: sequential au...
Amy R. Greenwald, Justin A. Boyan
AAAI
2012
11 years 7 months ago
Lagrangian Relaxation Techniques for Scalable Spatial Conservation Planning
We address the problem of spatial conservation planning in which the goal is to maximize the expected spread of cascades of an endangered species by strategically purchasing land ...
Akshat Kumar, XiaoJian Wu, Shlomo Zilberstein