6 papers
Diffusion enabled Optimal Transport distances for graph matching
Iman Seyedi, Francesco Archetti
This paper introduces Diffusion Semi-Relaxed Fused Gromov-Wasserstein (DsrFGW), a novel method for graph comparison that unifies node features and structural connectivity through o…
Wasserstein-enabled characterization of designs and myopic decisions in Bayesian Optimization
Antonio Candelieri, Francesco Archetti
Impractical assumptions, an inherently myopic nature, and the crucial role of the initial design, all together contribute to making theoretical convergence proofs of little value i…
Weighted Wasserstein Barycenter of Gaussian Processes for exotic Bayesian Optimization tasks
Antonio Candelieri, Francesco Archetti
Exploiting the analogy between Gaussian Distributions and Gaussian Processes' posterior, we present how the weighted Wasserstein Barycenter of Gaussian Processes (W2BGP) can be use…
Structural Vulnerability Assessment in Urban Transport Networks: A Network-Wide Geometric Approach Using Gromov-Wasserstein
Iman Seyedi, Antonio Candelieri, Enza Messina +1
Urban transportation networks are inherently vulnerable to disruptions that affect connectivity and passenger mobility. Traditional graph_theoretic metrics, such as betweenness and…
Gromov-Wasserstein and optimal transport: from assignment problems to probabilistic numeric
Iman Seyedi, Antonio Candelieri, Enza Messina +1
The assignment problem, a cornerstone of operations research, seeks an optimal one-to-one mapping between agents and tasks to minimize total cost. This work traces its evolution fr…
Wasserstein Barycenter Gaussian Process based Bayesian Optimization
Antonio Candelieri, Andrea Ponti, Francesco Archetti
Gaussian Process based Bayesian Optimization is a widely applied algorithm to learn and optimize under uncertainty, well-known for its sample efficiency. However, recently -- and m…