research
∙
08/21/2023
Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks
Interacting systems of events may exhibit cascading behavior where event...
share
research
∙
05/17/2023
Causal Discovery with Missing Data in a Multicentric Clinical Study
Causal inference for testing clinical hypotheses from observational data...
research
∙
05/17/2023
Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approach
Assessing the pre-operative risk of lymph node metastases in endometrial...
research
∙
05/17/2023
A Survey on Causal Discovery: Theory and Practice
Understanding the laws that govern a phenomenon is the core of scientifi...
research
∙
04/20/2022
Deep Reinforcement Learning for a Two-Echelon Supply Chain with Seasonal Demand
This paper leverages recent developments in reinforcement learning and d...
research
∙
11/24/2021
EAD: an ensemble approach to detect adversarial examples from the hidden features of deep neural networks
One of the key challenges in Deep Learning is the definition of effectiv...
research
∙
02/08/2021
Counterfactual Contextual Multi-Armed Bandit: a Real-World Application to Diagnose Apple Diseases
Post-harvest diseases of apple are one of the major issues in the econom...
research
∙
12/09/2020
Hard and Soft EM in Bayesian Network Learning from Incomplete Data
Incomplete data are a common feature in many domains, from clinical tria...
research
∙
07/07/2020
Constraint-Based Learning for Continuous-Time Bayesian Networks
Dynamic Bayesian networks have been well explored in the literature as d...
research
∙
02/17/2020
Investigating the Compositional Structure Of Deep Neural Networks
The current understanding of deep neural networks can only partially exp...
research
∙
04/18/2014