Zobrazeno 1 - 10
of 8 729
pro vyhledávání: '"A. Mussi"'
Autor:
Genalti, Gianmarco, Mussi, Marco, Gatti, Nicola, Restelli, Marcello, Castiglioni, Matteo, Metelli, Alberto Maria
Rested and Restless Bandits are two well-known bandit settings that are useful to model real-world sequential decision-making problems in which the expected reward of an arm evolves over time due to the actions we perform or due to the nature. In thi
Externí odkaz:
http://arxiv.org/abs/2409.05980
The increase of renewable energy generation towards the zero-emission target is making the problem of controlling power grids more and more challenging. The recent series of competitions Learning To Run a Power Network (L2RPN) have encouraged the use
Externí odkaz:
http://arxiv.org/abs/2409.04467
Constrained Reinforcement Learning (CRL) tackles sequential decision-making problems where agents are required to achieve goals by maximizing the expected return while meeting domain-specific constraints, which are often formulated as expected costs.
Externí odkaz:
http://arxiv.org/abs/2407.10775
We consider Kernelized Bandits (KBs) to optimize a function $f : \mathcal{X} \rightarrow [0,1]$ belonging to the Reproducing Kernel Hilbert Space (RKHS) $\mathcal{H}_k$. Mainstream works on kernelized bandits focus on a subgaussian noise model in whi
Externí odkaz:
http://arxiv.org/abs/2407.06321
Publikováno v:
European Journal of Mineralogy, Vol 32, Pp 13-26 (2020)
To apprehend plate tectonics and the dynamics of the lithosphere–asthenosphere boundary, composed principally of olivine, we need to understand the mechanisms that control plastic deformation of olivine in the relevant temperature domain. After mor
Externí odkaz:
https://doaj.org/article/eba39c7527e24ee38d6135247f31b01f
Policy gradient (PG) methods are successful approaches to deal with continuous reinforcement learning (RL) problems. They learn stochastic parametric (hyper)policies by either exploring in the space of actions or in the space of parameters. Stochasti
Externí odkaz:
http://arxiv.org/abs/2405.02235
Autor:
Mattjie, Christian, de Moura, Luis Vinicius, Ravazio, Rafaela Cappelari, Kupssinskü, Lucas Silveira, Parraga, Otávio, Delucis, Marcelo Mussi, Barros, Rodrigo Coelho
Segmentation in medical imaging is a critical component for the diagnosis, monitoring, and treatment of various diseases and medical conditions. Presently, the medical segmentation landscape is dominated by numerous specialized deep learning models,
Externí odkaz:
http://arxiv.org/abs/2305.00109
Autor:
Lancia, Giacomo, Durastanti, Claudio, Spitoni, Cristian, De Benedictis, Ilaria, Sciortino, Antonio, Cirillo, Emilio N. M., Ledda, Mario, Lisi, Antonella, Convertino, Annalisa, Mussi, Valentina
An early detection of different tumor subtypes is crucial for an effective guidance to personalized therapy. While much efforts focus on decoding the sequence of DNA basis to detect the genetic mutations related to cancer, it is becoming clear that p
Externí odkaz:
http://arxiv.org/abs/2302.08918
Autor:
Mussi, Marco, Montenegro, Alessandro, Trovó, Francesco, Restelli, Marcello, Metelli, Alberto Maria
Stochastic Rising Bandits (SRBs) model sequential decision-making problems in which the expected reward of the available options increases every time they are selected. This setting captures a wide range of scenarios in which the available options ar
Externí odkaz:
http://arxiv.org/abs/2302.07510
Autor:
Bacchiocchi, Francesco, Genalti, Gianmarco, Maran, Davide, Mussi, Marco, Restelli, Marcello, Gatti, Nicola, Metelli, Alberto Maria
Autoregressive processes naturally arise in a large variety of real-world scenarios, including stock markets, sales forecasting, weather prediction, advertising, and pricing. When facing a sequential decision-making problem in such a context, the tem
Externí odkaz:
http://arxiv.org/abs/2212.06251