14 citations · 41 across the 37 of their papers we have counts for
18 papers · 1 filter
Reinforcement Learning for Durable Algorithmic Recourse
Marina Ceccon, Alessandro Fabris, Goran Radanović +2
Algorithmic recourse seeks to provide individuals with actionable recommendations that increase their chances of receiving favorable outcomes from automated decision systems (e.g.,…
Towards Transparent and Efficient Anomaly Detection in Industrial Processes through ExIFFI
Davide Frizzo, Francesco Borsatti, Alessio Arcudi +3
Anomaly Detection (AD) is crucial in industrial settings to streamline operations by detecting underlying issues. Conventional methods merely label observations as normal or anomal…
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…
Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent
Luca Della Libera, Jacopo Andreoli, Davide Dalle Pezze +2
A crucial task in predictive maintenance is estimating the remaining useful life of physical systems. In the last decade, deep learning has improved considerably upon traditional m…
Heterogeneous Domain Adaptation and Equipment Matching: DANN-based Alignment with Cyclic Supervision (DBACS)
Natalie Gentner, Gian Antonio Susto
Process monitoring and control are essential in modern industries for ensuring high quality standards and optimizing production performance. These technologies have a long history…
Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters
Tommaso Barbariol, Davide Masiero, Enrico Feltresi +1
An Anomaly Detection (AD) System for Self-diagnosis has been developed for Multiphase Flow Meter (MPFM). The system relies on machine learning algorithms for time series forecastin…
A Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment Monitoring
Davide Dalle Pezze, Denis Deronjic, Chiara Masiero +3
Continual Learning aims to learn from a stream of tasks, being able to remember at the same time both new and old tasks. While many approaches were proposed for single-class classi…
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems
Elisa Marcelli, Tommaso Barbariol, Gian Antonio Susto
The detection of anomalous behaviours is an emerging need in many applications, particularly in contexts where security and reliability are critical aspects. While the definition o…
AcME -- Accelerated Model-agnostic Explanations: Fast Whitening of the Machine-Learning Black Box
David Dandolo, Chiara Masiero, Mattia Carletti +2
In the context of human-in-the-loop Machine Learning applications, like Decision Support Systems, interpretability approaches should provide actionable insights without making the…
TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenarios
Tommaso Barbariol, Gian Antonio Susto
Unsupervised anomaly detection tackles the problem of finding anomalies inside datasets without the labels availability; since data tagging is typically hard or expensive to obtain…
Lazy FSCA for Unsupervised Variable Selection
Federico Zocco, Marco Maggipinto, Gian Antonio Susto +1
Various unsupervised greedy selection methods have been proposed as computationally tractable approximations to the NP-hard subset selection problem. These methods rely on sequenti…
IntroVAC: Introspective Variational Classifiers for Learning Interpretable Latent Subspaces
Marco Maggipinto, Matteo Terzi, Gian Antonio Susto
Learning useful representations of complex data has been the subject of extensive research for many years. With the diffusion of Deep Neural Networks, Variational Autoencoders have…
-Variational Classifiers Under Attack
Marco Maggipinto, Matteo Terzi, Gian Antonio Susto
Deep Neural networks have gained lots of attention in recent years thanks to the breakthroughs obtained in the field of Computer Vision. However, despite their popularity, it has b…
Proximal Deterministic Policy Gradient
Marco Maggipinto, Gian Antonio Susto, Pratik Chaudhari
This paper introduces two simple techniques to improve off-policy Reinforcement Learning (RL) algorithms. First, we formulate off-policy RL as a stochastic proximal point iteration…
Adversarial Training Reduces Information and Improves Transferability
Matteo Terzi, Alessandro Achille, Marco Maggipinto +1
Recent results show that features of adversarially trained networks for classification, in addition to being robust, enable desirable properties such as invertibility. The latter p…
Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance
Mattia Carletti, Matteo Terzi, Gian Antonio Susto
Anomaly Detection is an unsupervised learning task aimed at detecting anomalous behaviours with respect to historical data. In particular, multivariate Anomaly Detection has an imp…
Directional Adversarial Training for Cost Sensitive Deep Learning Classification Applications
Matteo Terzi, Gian Antonio Susto, Pratik Chaudhari
In many real-world applications of Machine Learning it is of paramount importance not only to provide accurate predictions, but also to ensure certain levels of robustness. Adversa…