74 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Aldo Glielmo3 profiles9 · h 5
- A. Laio5 · h 55
- Luigi Bellomarini2 profiles5 · h 14
- Emanuel Sallinger3 · h 15
- Iuri Macocco3 · h 5
- Luca Guarnera3 · h 15
- Marco Favorito2 profiles3 · h 12
- Oliver Giudice3 · h 15
- S. Battiato3 · h 43
- Alex Rodriguez2 profiles2 · h 18
- A. Mercatanti2 · h 10
- Andrea Coletta2 profiles2 · h 2
- Scuola Internazionale Superiore di Studi AvanzatiIT5 papers
- Sapienza University of RomeIT4 papers
- The Abdus Salam International Centre for Theoretical Physics (ICTP)IT3 papers
- TU WienAT3 papers
- University of CataniaIT3 papers
- University of OxfordGB3 papers
- Duke UniversityUS2 papers
- ETH ZurichCH2 papers
- Roma Tre UniversityIT2 papers
- Università Cattolica del Sacro CuoreIT2 papers
- University of Milano-BicoccaIT2 papers
- University of TriesteIT2 papers
5 papers · 1 filter
Fast and Stable Credit Gamma of CVA
Roberto Daluiso
Credit Valuation Adjustment is a balance sheet item which is nowadays subject to active risk management by specialized traders. However, one of the most important risk factors, whi…
PG-Triggers: Triggers for Property Graphs
Stefano Ceri, Anna Bernasconi, Alessia Gagliardi +3
Graph databases are emerging as the leading data management technology for storing large knowledge graphs; significant efforts are ongoing to produce new standards (such as the Gra…
Level Up the Deepfake Detection: a Method to Effectively Discriminate Images Generated by GAN Architectures and Diffusion Models
Luca Guarnera, Oliver Giudice, Sebastiano Battiato
The image deepfake detection task has been greatly addressed by the scientific community to discriminate real images from those generated by Artificial Intelligence (AI) models: a…
Exploiting Multiple Abstractions in Episodic RL via Reward Shaping
Roberto Cipollone, Giuseppe De Giacomo, Marco Favorito +2
One major limitation to the applicability of Reinforcement Learning (RL) to many practical domains is the large number of samples required to learn an optimal policy. To address th…
Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMs
Aldo Glielmo, Marco Favorito, Debmallya Chanda +1
Calibrating agent-based models (ABMs) in economics and finance typically involves a derivative-free search in a very large parameter space. In this work, we benchmark a number of s…