Search Results for author:
Found 17 papers, 2 papers with code
A Bayesian approach for prompt optimization in pre-trained language models
In this paper we propose a Bayesian optimization method, executed in a continuous em-bedding of the combinatorial space.
Mastering the exploration-exploitation trade-off in Bayesian Optimization
Gaussian Process based Bayesian Optimization is a well-known sample efficient sequential strategy for globally optimizing black-box, expensive, and multi-extremal functions.
Gaussian Process regression over discrete probability measures: on the non-stationarity relation between Euclidean and Wasserstein Squared Exponential Kernels
Gaussian Process regression is a kernel method successfully adopted in many real-life applications.
BORA: Bayesian Optimization for Resource Allocation
In this paper we propose (i) an extension of the optimal resource allocation to a more general class of problems, specifically with resources availability changing over time, and (ii) Bayesian Optimization as a more efficient alternative to SBF.
Fair and Green Hyperparameter Optimization via Multi-objective and Multiple Information Source Bayesian Optimization
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.
Gamifying optimization: a Wasserstein distance-based analysis of human search
A distance from the Pareto frontier determines whether a choice is Pareto rational.
Bayesian Optimization and Deep Learning forsteering wheel angle prediction
Automated driving systems (ADS) have undergone a significant improvement in the last years.
OCTIS: Comparing and Optimizing Topic models is Simple!
In this paper, we present OCTIS, a framework for training, analyzing, and comparing Topic Models, whose optimal hyper-parameters are estimated using a Bayesian Optimization approach.
Risk Aware Optimization of Water Sensor Placement
A bi-objective formalization is proposed: minimizing the average MDT and its standard deviation, that is the risk to detect some contamination event too late than the average MDT.
MISO-wiLDCosts: Multi Information Source Optimization with Location Dependent Costs
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
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 gathering (exploration) and reward seeking (exploitation).
Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimization
Computational results are reported related to the optimization of the hyperparameters of a Support Vector Machine (SVM) classifier using two sources: a large dataset - the most expensive one - and a smaller portion of it.
Composition of kernel and acquisition functions for High Dimensional Bayesian Optimization
This ap-proach makes more efficient the learning/updating of the probabilistic surrogate model and allows an efficient optimization of the acquisition function.
Modelling Human Active Search in Optimizing Black-box Functions
Modelling human function learning has been the subject of in-tense research in cognitive sciences.
Safe global optimization of expensive noisy black-box functions in the $δ$-Lipschitz framework
The notion "safe" means that the objective function $f(x)$ during optimization should not violate a "safety" threshold, for instance, a certain a priori given value $h$ in a maximization problem.
Efficient Kernel-based Subsequence Search for User Identification from Walking Activity
This paper presents an efficient approach for subsequence search in data streams.
Reachability-based safe learning for optimal control problem
On the basis of the known part of the model, a safe set in which the system can learn safely, the algorithm can choose optimal actions for pursuing the target set as long as the safety-preserving condition is satisfied.
