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The equilibrium effects of mortality risk
This paper investigates how mortality risk affects agents’ optimal decisions and asset prices within a general equilibrium framework. A risk-averse households facing a... -
Private Online learning of order flow and market impact with Bayesian change-point de...
The paper proposes the use of Bayesian online change-point detection (BOCPD) methods to identify regime shifts in real-time and enable online predictions of order flow and... -
Private Bayesian autoregressive online change-point detection with time-varying param...
The paper proposes a new methodology on Change-Point Detection in a probabilistic framework that allows for real time updates and uncertainty estimation. It allows to... -
Private CAESar: Conditional Autoregressive Expected Shortfall
The paper proposes a new methodology named Conditional Autoregressive Expected Shortfall (CAESar) which is able to handle dynamic patterns flexibly and includes... -
Private Reinforcement Learning for Optimal Execution When Liquidity Is Time-Varying
The paper investigates the application of Double Deep Q-Learning, a neural network-based Reinforcement Learning approach, to derive optimal trading policies in the presence of... -
Private Information flow in the FTX bankruptcy: A network approach
The paper investigates the cryptocurrency network of the FTX exchange during the collapse of its native token. A filtered correlation matrix is constructed by using... -
Private Modeling shock propagation and resilience in financial temporal networks
The paper proposes a vector autoregressive framework to analytically compute the Impulse Response Function of a network metric conditional to a shock on a node. Moreover, the... -
Code and data accompanying the paper: Quantifying Privacy Risks in Synthetic ...
This repository contains the code and data for the paper “Quantifying Privacy Risks in Synthetic Data: A Study on Black-Box Membership Inference”. It enables full... -
Experimenting ASPen on the Pokémon dataset
It has been recently proposed ASPen: an Answer Set Programming (ASP) encoding for LACE, which is a novel declarative approach to Collective Entity Resolution in the classical... -
Private An Inverse Learning Paradigm for Controller Tuning Rules
A sim2real approach for data-driven controller tuning, utilizing a digital twin to generate input-output data and suitable controllers around nominal parameter values. It is a... -
Experimenting ASPen on the Music dataset
It has been recently proposed ASPen: an Answer Set Programming (ASP) encoding for LACE, which is a novel declarative approach to Collective Entity Resolution in the classical... -
Experimenting ASPen on the Cora dataset
It has been recently proposed ASPen: an Answer Set Programming (ASP) encoding for LACE, which is a novel declarative approach to Collective Entity Resolution in the classical... -
Private Toward eXplainabile Data-Driven Control (XDDC): The Property-Preserving Frame...
As Artificial Intelligence (AI) techniques continue to advance, the need for explainability becomes increasingly crucial, especially in sensitive or safety-critical domains.... -
Private Meta-learning for model-reference data-driven control
One-shot direct model-reference control design techniques, like the Virtual Reference Feedback Tuning (VRFT) approach, offer time-saving solutions for calibrating... -
Experimenting ASPen on the IMDB dataset
It has been recently proposed ASPen: an Answer Set Programming (ASP) encoding for LACE, which is a novel declarative approach to Collective Entity Resolution in the classical... -
Experimenting ASPen on the DBLP dataset
It has been recently proposed ASPen: an Answer Set Programming (ASP) encoding for LACE, which is a novel declarative approach to Collective Entity Resolution in the classical... -
Private Environmental Monitoring of Fluorescence Response
We study a novel sequential decision-making setting, namely the dissimilarity bandits. At each round, the learner pulls an arm that provides a stochastic d-dimensional... -
Experimental results from the Empirical Investigation of the Completeness of ...
This is the raw data from the empirical investigation of the paper “Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation”. This work aim of... -
Private Optimizing Empty Container Repositioning and Fleet Deployment via Configurabl...
We introduce a novel framework, Configurable SemiPOMDPs, to model this type of problems. Furthermore, we provide a two-stage learning algorithm, “Configure & Conquer”... -
EnviroStream (Benchmark)
Stream Reasoning (SR) focuses on developing advanced approaches for applying inference to dynamic data streams; it has become increasingly relevant in various application...
