5 citations · 7 across the 10 of their papers we have counts for
12 papers
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…
Graph data augmentation with Gromow-Wasserstein Barycenters
Andrea Ponti
Graphs are ubiquitous in various fields, and deep learning methods have been successful applied in graph classification tasks. However, building large and diverse graph datasets fo…
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…
Multi-Task Learning on Networks
Andrea Ponti
The multi-task learning (MTL) paradigm can be traced back to an early paper of Caruana (1997) in which it was argued that data from multiple tasks can be used with the aim to obtai…
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…
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…
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…