2 citations · 4 across the 4 of their papers we have counts for
6 papers
Adaptive Annealed Importance Sampling with Constant Rate Progress
Shirin Goshtasbpour, Victor Cohen, Fernando Perez-Cruz
Annealed Importance Sampling (AIS) synthesizes weighted samples from an intractable distribution given its unnormalized density function. This algorithm relies on a sequence of int…
Future memories are not needed for large classes of POMDPs
Victor Cohen, Axel Parmentier
Optimal policies for partially observed Markov decision processes (POMDPs) are history-dependent: Decisions are made based on the entire history of observation. Memoryless policies…
Integer programming for weakly coupled stochastic dynamic programs with partial information
Victor Cohen, Axel Parmentier
This paper introduces algorithms for problems where a decision maker has to control a system composed of several components and has access to only partial information on the state…
Two generalizations of Markov blankets
Victor Cohen, Axel Parmentier
In a probabilistic graphical model on a set of variables , the Markov blanket of a random vector is the minimal set of variables conditioned to which is independent from…
Integer programming on the junction tree polytope for influence diagrams
Axel Parmentier, Victor Cohen, Vincent Leclère +2
Influence Diagrams (ID) are a flexible tool to represent discrete stochastic optimization problems, including Markov Decision Process (MDP) and Partially Observable MDP as standard…
Linear Programming for Decision Processes with Partial Information
Victor Cohen, Axel Parmentier
Markov Decision Processes (MDPs) are stochastic optimization problems that model situations where a decision maker controls a system based on its state. Partially observed Markov d…