5 papers
Finite-Time Guarantees for Constrained Reinforcement Learning under Partial Observability
Valentina Zaccaria, Philip Jordan, Tingting Ni +2
We study reinforcement learning in constrained partially observable Markov decision processes (CPOMDPs), where an agent maximizes reward subject to a cost constraint using only inc…
Explainable Visual Anomaly Detection via Concept Bottleneck Models
Arianna Stropeni, Valentina Zaccaria, Francesco Borsatti +3
In recent years, VAD (Visual Anomaly Detection) has attracted significant attention due to its high interpretability, as it provides visual explanations by highlighting anomalous r…
Fault Identification Enhancement with Reinforcement Learning (FIERL)
Valentina Zaccaria, Davide Sartor, Simone Del Favero +1
This letter presents a novel approach in the field of Active Fault Detection (AFD), by explicitly separating the task into two parts: Passive Fault Detection (PFD) and control inpu…
Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD
Valentina Zaccaria, Chiara Masiero, David Dandolo +1
While Machine Learning has become crucial for Industry 4.0, its opaque nature hinders trust and impedes the transformation of valuable insights into actionable decision, a challeng…
AcME-AD: Accelerated Model Explanations for Anomaly Detection
Valentina Zaccaria, David Dandolo, Chiara Masiero +1
Pursuing fast and robust interpretability in Anomaly Detection is crucial, especially due to its significance in practical applications. Traditional Anomaly Detection methods excel…