8 citations · 9 across the 4 of their papers we have counts for
9 papers
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…
Optimal Counterfactual Explanations in Tree Ensembles
Axel Parmentier, Thibaut Vidal
Counterfactual explanations are usually generated through heuristics that are sensitive to the search's initial conditions. The absence of guarantees of performance and robustness…
Learning to solve the single machine scheduling problem with release times and sum of completion times
Axel Parmentier, Vincent T'Kindt
In this paper, we focus on the solution of a hard single machine scheduling problem by new heuristic algorithms embedding techniques from machine learning field and scheduling theo…
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…