226 citations · 541 across the 30 of their papers we have counts for
4 papers · 1 filter
Monte-Carlo optimizations for resource allocation problems in stochastic network systems
Milos Hauskrecht, Tomas Singliar
Real-world distributed systems and networks are often unreliable and subject to random failures of its components. Such a stochastic behavior affects adversely the complexity of op…
Variational Dual-Tree Framework for Large-Scale Transition Matrix Approximation
Saeed Amizadeh, Bo Thiesson, Milos Hauskrecht
In recent years, non-parametric methods utilizing random walks on graphs have been used to solve a wide range of machine learning problems, but in their simplest form they do not s…
Solving Factored MDPs with Continuous and Discrete Variables
Carlos E. Guestrin, Milos Hauskrecht, Branislav Kveton
Although many real-world stochastic planning problems are more naturally formulated by hybrid models with both discrete and continuous variables, current state-of-the-art methods c…
Partitioned Linear Programming Approximations for MDPs
Branislav Kveton, Milos Hauskrecht
Approximate linear programming (ALP) is an efficient approach to solving large factored Markov decision processes (MDPs). The main idea of the method is to approximate the optimal…