activity
20122023
most citedMulti-fidelity modeling with different input domain definitions using Deep Gaussian Processes

51 citations · 66 across the 6 of their papers we have counts for

collaborators

7 papers

cs.AI2023

A Virtual-Force Based Swarm Algorithm for Balanced Circular Bin Packing Problems

Juliette Gamot, Mathieu Balesdent, Romain Wuilbercq +3

Balanced circular bin packing problems consist in positioning a given number of weighted circles in order to minimize the radius of a circular container while satisfying equilibriu…

cs.AI2022★ 1 cited

Hidden-Variables Genetic Algorithm for Variable-Size Design Space Optimal Layout Problems with Application to Aerospace Vehicles

Juliette Gamot, Mathieu Balesdent, Arnault Tremolet +3

The optimal layout of a complex system such as aerospace vehicles consists in placing a given number of components in a container in order to minimize one or several objectives und…

cs.LG2020★ 51 cited

Multi-fidelity modeling with different input domain definitions using Deep Gaussian Processes

Ali Hebbal, Loic Brevault, Mathieu Balesdent +2

Multi-fidelity approaches combine different models built on a scarce but accurate data-set (high-fidelity data-set), and a large but approximate one (low-fidelity data-set) in orde…

stat.ML2019★ 14 cited

Bayesian Optimization using Deep Gaussian Processes

Ali Hebbal, Loic Brevault, Mathieu Balesdent +2

Bayesian Optimization using Gaussian Processes is a popular approach to deal with the optimization of expensive black-box functions. However, because of the a priori on the station…

math.OC2018

Efficient Global Optimization using Deep Gaussian Processes

Ali Hebbal, Loic Brevault, Mathieu Balesdent +2

Efficient Global Optimization (EGO) is widely used for the optimization of computationally expensive black-box functions. It uses a surrogate modeling technique based on Gaussian P…

cs.DC2012

A GPU-accelerated Branch-and-Bound Algorithm for the Flow-Shop Scheduling Problem

Melab Nouredine, Imen Chakroun, Mezmaz Mohand +1

Branch-and-Bound (B&B) algorithms are time intensive tree-based exploration methods for solving to optimality combinatorial optimization problems. In this paper, we investigate the…

cs.DC2012

An Adaptative Multi-GPU based Branch-and-Bound. A Case Study: the Flow-Shop Scheduling Problem

Imen Chakroun, Nouredine Melab

Solving exactly Combinatorial Optimization Problems (COPs) using a Branch-and-Bound (B&B) algorithm requires a huge amount of computational resources. Therefore, we recently invest…