Antonio Candelieri
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View article: Distributionally Robust Bayesian Optimization via Sinkhorn-Based Wasserstein Barycenter
Distributionally Robust Bayesian Optimization via Sinkhorn-Based Wasserstein Barycenter Open
This paper introduces a novel framework for Distributionally Robust Bayesian Optimization (DRBO) with continuous context that integrates optimal transport theory and entropic regularization. We propose the sampling from the Wasserstein Bar…
View article: Wasserstein Distributionally Robust Optimization for Chance Constrained Facility Location Under Uncertain Demand
Wasserstein Distributionally Robust Optimization for Chance Constrained Facility Location Under Uncertain Demand Open
The purpose of this paper is to present a novel optimization framework that enhances Wasserstein Distributionally Robust Optimization (WDRO) for chance-constrained facility location problems under demand uncertainty. Traditional methods of…
View article: Wasserstein Barycenter Gaussian Process based Bayesian Optimization
Wasserstein Barycenter Gaussian Process based Bayesian Optimization Open
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 more frequently -- research studies have empirically demonst…
View article: Gaussian Process regression over discrete probability measures: on the non-stationarity relation between Euclidean and Wasserstein Squared Exponential Kernels
Gaussian Process regression over discrete probability measures: on the non-stationarity relation between Euclidean and Wasserstein Squared Exponential Kernels Open
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 input spaces, like the one considered in this paper, consis…
View article: Bayesian Optimization for Instruction Generation
Bayesian Optimization for Instruction Generation Open
The performance of Large Language Models (LLMs) strongly depends on the selection of the best instructions for different downstream tasks, especially in the case of black-box LLMs. This study introduces BOInG (Bayesian Optimization for Ins…
View article: Bayesian Optimization Using Simulation-Based Multiple Information Sources over Combinatorial Structures
Bayesian Optimization Using Simulation-Based Multiple Information Sources over Combinatorial Structures Open
Bayesian optimization due to its flexibility and sample efficiency has become a standard approach for simulation optimization. To reduce this problem, one can resort to cheaper surrogates of the objective function. Examples are ubiquitous,…
View article: Prompt Optimization in Large Language Models
Prompt Optimization in Large Language Models Open
Prompt optimization is a crucial task for improving the performance of large language models for downstream tasks. In this paper, a prompt is a sequence of n-grams selected from a vocabulary. Consequently, the aim is to select the optimal …
View article: Wasserstein-Enabled Leaks Localization in Water Distribution Networks
Wasserstein-Enabled Leaks Localization in Water Distribution Networks Open
Leaks in water distribution networks are estimated to account for up to 30% of the total distributed water; moreover, the increasing demand and the skyrocketing energy cost have made leak localization and adoption ever more important to wa…
View article: Towards Full Forward On-Tiny-Device Learning: A Guided Search for a Randomly Initialized Neural Network
Towards Full Forward On-Tiny-Device Learning: A Guided Search for a Randomly Initialized Neural Network Open
In the context of TinyML, many research efforts have been devoted to designing forward topologies to support On-Device Learning. Reaching this target would bring numerous advantages, including reductions in latency and computational comple…
View article: A Bayesian approach for prompt optimization in pre-trained language models
A Bayesian approach for prompt optimization in pre-trained language models Open
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 the sequence of tokens: in this paper we formulate it as a co…
View article: Safe Optimal Control of Dynamic Systems: Learning from Experts and Safely Exploring New Policies
Safe Optimal Control of Dynamic Systems: Learning from Experts and Safely Exploring New Policies Open
Many real-life systems are usually controlled through policies replicating experts’ knowledge, typically favouring “safety” at the expense of optimality. Indeed, these control policies are usually aimed at avoiding a system’s disruptions o…
View article: Bayesian optimization over the probability simplex
Bayesian optimization over the probability simplex Open
Gaussian Process based Bayesian Optimization is largely adopted for solving problems where the inputs are in Euclidean spaces. In this paper we associate the inputs to discrete probability distributions which are elements of the probabilit…
View article: Intrusion Detection in Networks by Wasserstein Enabled Many-Objective Evolutionary Algorithms
Intrusion Detection in Networks by Wasserstein Enabled Many-Objective Evolutionary Algorithms Open
This manuscript explores the problem of deploying sensors in networks to detect intrusions as effectively as possible. In water distribution networks, intrusions can cause a spread of contaminants over the whole network; we are searching f…
View article: Mastering the exploration-exploitation trade-off in Bayesian Optimization
Mastering the exploration-exploitation trade-off in Bayesian Optimization Open
Gaussian Process based Bayesian Optimization is a well-known sample efficient sequential strategy for globally optimizing black-box, expensive, and multi-extremal functions. The role of the Gaussian Process is to provide a probabilistic ap…
View article: On the Generalization of Projection-Based Gender Debiasing in Word Embedding
On the Generalization of Projection-Based Gender Debiasing in Word Embedding Open
Gender bias estimation and mitigation techniques in word embeddings lack an understanding of their generalization capabilities. In this work, we complement prior research by comparing in a systematic way four gender bias metrics (Word Embe…
View article: Explaining Exploration–Exploitation in Humans
Explaining Exploration–Exploitation in Humans Open
Human as well as algorithmic searches are performed to balance exploration and exploitation. The search task in this paper is the global optimization of a 2D multimodal function, unknown to the searcher. Thus, the task presents the followi…
View article: Gaussian Process regression over discrete probability measures: on the non-stationarity relation between Euclidean and Wasserstein Squared Exponential Kernels
Gaussian Process regression over discrete probability measures: on the non-stationarity relation between Euclidean and Wasserstein Squared Exponential Kernels Open
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 input spaces, like the one considered in this paper, consis…
View article: The “Unreasonable” Effectiveness of the Wasserstein Distance in Analyzing Key Performance Indicators of a Network of Stores
The “Unreasonable” Effectiveness of the Wasserstein Distance in Analyzing Key Performance Indicators of a Network of Stores Open
Large retail companies routinely gather huge amounts of customer data, which are to be analyzed at a low granularity. To enable this analysis, several Key Performance Indicators (KPIs), acquired for each customer through different channels…
View article: BORA: Bayesian Optimization for Resource Allocation
BORA: Bayesian Optimization for Resource Allocation Open
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 Feedback (SBF) is the reference method for efficiently solvi…
View article: Exploring telediagnostic procedures in child neuropsychiatry: addressing ADHD diagnosis and autism symptoms through supervised machine learning
Exploring telediagnostic procedures in child neuropsychiatry: addressing ADHD diagnosis and autism symptoms through supervised machine learning Open
Recently there has been an increase in telemedicine applied to child neuropsychiatry, such as the use of online platforms to remotely collect anamnestic and behavioral information. In the present proof-of-concept study, we aimed to underst…
View article: Towards telediagnostic procedures in child neuropsychiatry: addressing ADHD diagnosis and autism symptoms through supervised machine learning
Towards telediagnostic procedures in child neuropsychiatry: addressing ADHD diagnosis and autism symptoms through supervised machine learning Open
Recently there has been an increase in telemedicine applied to child neuropsychiatry, such as the use of online platforms to remotely collect anamnestic and behavioral information. In the present proof-of-concept study, we aimed to underst…
View article: Bayesian optimization and deep learning for steering wheel angle prediction
Bayesian optimization and deep learning for steering wheel angle prediction Open
Automated driving systems (ADS) have undergone a significant improvement in the last years. ADS and more precisely self-driving cars technologies will change the way we perceive and know the world of transportation systems in terms of user…
View article: Fair and Green Hyperparameter Optimization via Multi-objective and Multiple Information Source Bayesian Optimization
Fair and Green Hyperparameter Optimization via Multi-objective and Multiple Information Source Bayesian Optimization Open
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 decision supports. Recently, multi-objective hyperparameter opti…
View article: Lost in Optimization of Water Distribution Systems: Better Call Bayes
Lost in Optimization of Water Distribution Systems: Better Call Bayes Open
The main goal of this paper is to show that Bayesian optimization can be regarded as a general framework for the data-driven modelling and solution of problems arising in water distribution systems. Scenario-based hydraulic simulation and …
View article: Lost in Optimization of Water Distribution Systems: Better Call Bayes
Lost in Optimization of Water Distribution Systems: Better Call Bayes Open
The main goal of this paper is to show that Bayesian optimization could be regarded as a general framework for the data driven modelling and solution of problems arising in water distribution systems. Hydraulic simulation, both scenario ba…