5 citations · 9 across the 17 of their papers we have counts for
20 papers
Fused ultrametric Gromov-Wasserstein for Scenario Tree generation in Multistage Stochastic Programming
Antonio Candelieri, Iman Seyedi, Francesco Archetti
Multistage Stochastic Programming requires scenario trees that accurately approximate the unknown underlying uncertainty process without violating the non-anticipativity constraint…
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…
A Bayesian approach for prompt optimization in pre-trained language models
Antonio Sabbatella, Andrea Ponti, Antonio Candelieri +2
A prompt is a sequence of symbol or tokens, selected from a vocabulary according to some rule, which is prepended/concatenated to a textual query. A key problem is how to select th…
Gaussian Process regression over discrete probability measures: on the non-stationarity relation between Euclidean and Wasserstein Squared Exponential Kernels
Antonio Candelieri, Andrea Ponti, Francesco Archetti
Gaussian Process regression is a kernel method successfully adopted in many real-life applications. Recently, there is a growing interest on extending this method to non-Euclidean…
BORA: Bayesian Optimization for Resource Allocation
Antonio Candelieri, Andrea Ponti, Francesco Archetti
Optimal resource allocation is gaining a renewed interest due its relevance as a core problem in managing, over time, cloud and high-performance computing facilities. Semi-Bandit F…
Fair and Green Hyperparameter Optimization via Multi-objective and Multiple Information Source Bayesian Optimization
Antonio Candelieri, Andrea Ponti, Francesco Archetti
There is a consensus that focusing only on accuracy in searching for optimal machine learning models amplifies biases contained in the data, leading to unfair predictions and decis…
Gamifying optimization: a Wasserstein distance-based analysis of human search
Antonio Candelieri, Andrea Ponti, Francesco Archetti
The main objective of this paper is to outline a theoretical framework to characterise humans' decision-making strategies under uncertainty, in particular active learning in a blac…
Risk Aware Optimization of Water Sensor Placement
Antonio Candelieri, Andrea Ponti, Francesco Archetti
Optimal sensor placement (SP) usually minimizes an impact measure, such as the amount of contaminated water or the number of inhabitants affected before detection. The common choic…
MISO-wiLDCosts: Multi Information Source Optimization with Location Dependent Costs
Antonio Candelieri, Francesco Archetti
This paper addresses black-box optimization over multiple information sources whose both fidelity and query cost change over the search space, that is they are location dependent.…
Uncertainty quantification and exploration-exploitation trade-off in humans
Antonio Candelieri, Andrea Ponti, Francesco Archetti
The main objective of this paper is to outline a theoretical framework to analyse how humans' decision-making strategies under uncertainty manage the trade-off between information…
Resilience in urban networked infrastructure: the case of Water Distribution Systems
Antonio Candelieri, Ilaria Giordani, Andrea Ponti +1
Resilience is meant as the capability of a networked infrastructure to provide its service even if some components fail: in this paper we focus on how resilience depends both on ne…
Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimization
Antonio Candelieri, Riccardo Perego, Francesco Archetti
Searching for accurate Machine and Deep Learning models is a computationally expensive and awfully energivorous process. A strategy which has been gaining recently importance to dr…
Modelling Human Active Search in Optimizing Black-box Functions
Antonio Candelieri, Riccardo Perego, Ilaria Giordani +2
Modelling human function learning has been the subject of in-tense research in cognitive sciences. The topic is relevant in black-box optimization where information about the objec…
Learning Optimal Control of Water Distribution Networks through Sequential Model-based Optimization
Antonio Candelieri, Bruno Galuzzi, Ilaria Giordani +1
Sequential Model-based Bayesian Optimization has been successful-ly applied to several application domains, characterized by complex search spaces, such as Automated Machine Learni…
Composition of kernel and acquisition functions for High Dimensional Bayesian Optimization
Antonio Candelieri, Ilaria Giordani, Riccardo Perego +1
Bayesian Optimization has become the reference method for the global optimization of black box, expensive and possibly noisy functions. Bayesian Op-timization learns a probabilisti…