283 citations · 802 across the 44 of their papers we have counts for
49 papers
Information-Based Calibration of Uncertainty Quantification in Product-of-Experts Gaussian Process Models
Yean Hoon Ong, Paolo Barucca, Wei Pan +1
Gaussian process (GP) regression with a single global GP (GP-glo) incurs cubic computational cost, limiting scalability to large datasets. Product-of-experts GP models (GP-pro), wh…
Characterizing Optimizer-Dependent Training Dynamics Through Hessian Eigenvector Displacement and Localization
Marcelina Marjankowska, Valerio Modugno, Paolo Barucca
Hessian spectral properties are a standard tool in analysing neural-network training, with eigenvalues linked to sharpness, generalization, and optimization dynamics. Eigenvalues q…
Fundamental Limits of Stability Inference in High-Dimensional Complex Systems
Michela Costa, Kentaro Hoshisashi, Flaviano Morone +2
Many complex systems, including ecosystems, neural circuits, and financial markets, are inferred to operate close to a threshold of instability, at which a small perturbation can p…
Physics-Informed Neural Networks for Solving Derivative-Constrained PDEs
Kentaro Hoshisashi, Carolyn E Phelan, Paolo Barucca
Physics-Informed Neural Networks (PINNs) recast PDE solving as an optimisation problem in function space by minimising a residual-based objective, yet many applications require add…
Maximum entropy temporal networks
Paolo Barucca
Temporal networks consist of timestamped directed interactions that may appear continuously in time, yet few studies have directly tackled the continuous-time modeling of networks.…
Parsimonious Hawkes Processes for temporal networks modelling
Yuwei Zhu, Paolo Barucca
Temporal networks are characterised by interdependent link events between nodes, forming ordered sequences of links that may represent specific information flows in the system. Nev…
How low-cost AI universal approximators reshape market efficiency
Paolo Barucca, Flaviano Morone
The efficient market hypothesis (EMH) famously stated that prices fully reflect the information available to traders. This critically depends on the transfer of information into pr…
Granger Causality Detection with Kolmogorov-Arnold Networks
Hongyu Lin, Mohan Ren, Paolo Barucca +1
Discovering causal relationships in time series data is central in many scientific areas, ranging from economics to climate science. Granger causality is a powerful tool for causal…
How the interplay between power concentration, competition, and propagation affects the resource efficiency of distributed ledgers
Paolo Barucca, Carlo Campajola, Jiahua Xu
Forks in the Bitcoin network result from the natural competition in the blockchain's Proof-of-Work consensus protocol. Their frequency is a critical indicator for the efficiency of…
Whack-a-mole Online Learning: Physics-Informed Neural Network for Intraday Implied Volatility Surface
Kentaro Hoshisashi, Carolyn E. Phelan, Paolo Barucca
Calibrating the time-dependent Implied Volatility Surface (IVS) using sparse market data is an essential challenge in computational finance, particularly for real-time applications…
Random matrix ensemble for the covariance matrix of Ornstein-Uhlenbeck processes with heterogeneous temperatures
Leonardo Ferreira, Fernando Metz, Paolo Barucca
We introduce a random matrix model for the stationary covariance of multivariate Ornstein-Uhlenbeck processes with heterogeneous temperatures, where the covariance is constrained b…
Predicting public market behavior from private equity deals
Paolo Barucca, Flaviano Morone
We process private equity transactions to predict public market behavior with a logit model. Specifically, we estimate our model to predict quarterly returns for both the broad mar…
No-Arbitrage Deep Calibration for Volatility Smile and Skewness
Kentaro Hoshisashi, Carolyn E. Phelan, Paolo Barucca
Volatility smile and skewness are two key properties of option prices that are represented by the implied volatility (IV) surface. However, IV surface calibration through nonlinear…
Modelling Equity Transaction Networks as Bursty Processes
Isobel Seabrook, Paolo Barucca, Fabio Caccioli
Trade executions for major stocks come in bursts of activity, which can be partly attributed to the presence of self- and mutual excitations endogenous to the system. In this paper…
Deep Reinforcement Learning for Optimal Investment and Saving Strategy Selection in Heterogeneous Profiles: Intelligent Agents working towards retirement
Fatih Ozhamaratli, Paolo Barucca
The transition from defined benefit to defined contribution pension plans shifts the responsibility for saving toward retirement from governments and institutions to the individual…
Variational Heteroscedastic Volatility Model
Zexuan Yin, Paolo Barucca
We propose Variational Heteroscedastic Volatility Model (VHVM) -- an end-to-end neural network architecture capable of modelling heteroscedastic behaviour in multivariate financial…
Deep Recurrent Modelling of Granger Causality with Latent Confounding
Zexuan Yin, Paolo Barucca
Inferring causal relationships in observational time series data is an important task when interventions cannot be performed. Granger causality is a popular framework to infer pote…
Neural Generalised AutoRegressive Conditional Heteroskedasticity
Zexuan Yin, Paolo Barucca
We propose Neural GARCH, a class of methods to model conditional heteroskedasticity in financial time series. Neural GARCH is a neural network adaptation of the GARCH 1,1 model in…
Sequential asset ranking in nonstationary time series
Gabriel Borrageiro
We create a ranking algorithm, the naive Bayes asset ranker. Our algorithm computes the posterior probability that individual assets will be ranked higher than other portfolio cons…
Structural importance and evolution: an application to financial transaction networks
Isobel Seabrook, Paolo Barucca, Fabio Caccioli
A fundamental problem in the study of networks is the identification of important nodes. This is typically achieved using centrality metrics, which rank nodes in terms of their pos…
The Recurrent Reinforcement Learning Crypto Agent
Gabriel Borrageiro, Nick Firoozye, Paolo Barucca
We demonstrate a novel application of online transfer learning for a digital assets trading agent. This agent uses a powerful feature space representation in the form of an echo st…
Consensus formation on heterogeneous networks
Edoardo Fadda, Junda He, Claudio Tessone +1
Reaching consensus -- a macroscopic state where the system constituents display the same microscopic state -- is a necessity in multiple complex socio-technical and techno-economic…
Reinforcement Learning for Systematic FX Trading
Gabriel Borrageiro, Nick Firoozye, Paolo Barucca
We explore online inductive transfer learning, with a feature representation transfer from a radial basis function network formed of Gaussian mixture model hidden processing units…
A generative model for age and income distribution
Fatih Ozhamaratli, Oleg Kitov, Paolo Barucca
Each individual in society experiences an evolution of their income during their lifetime. Macroscopically, this dynamics creates a statistical relationship between age and income…