5 citations · 11 across the 38 of their papers we have counts for
17 papers · 1 filter
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…
Uncertainty and Business-Aware Remaining Useful Life Estimation for Semiconductor Manufacturing
Davide Frizzo, Francesco Borsatti, Gian Antonio Susto
Semiconductor manufacturing relies on tightly interconnected components, so early identification of the assets most likely to fail is essential to prevent a single breakdown from d…
TypiCore: A Hybrid Active Query Strategy for Class-Incremental Learning on Time Series
Gabor Szucs, Samuel Jacsev, Marcell Nemeth +2
Time series data play a pivotal role across numerous domains, including healthcare and manufacturing. In real-world environments, models must cope with distribution shifts over tim…
Balancing Efficiency and Fairness in Traffic Light Control through Deep Reinforcement Learning
Matteo Cederle, Giacomo Scatto, Gian Antonio Susto
Urban traffic congestion presents a significant challenge for modern cities, which impacts mobility and sustainability. Traditional traffic light control systems often fail to adap…
Recurrent Deep Reinforcement Learning for Chemotherapy Control under Partial Observability
Firas Mohamed Elamine Kiram, Imane Youkana, Rachida Saouli +2
Chemotherapy dose optimization can be formulated as a dynamic treatment regime, requiring sequential decisions under uncertainty that must balance tumor suppression against toxicit…
Towards Batch-to-Streaming Deep Reinforcement Learning for Continuous Control
Riccardo De Monte, Matteo Cederle, Gian Antonio Susto
State-of-the-art deep reinforcement learning (RL) methods have achieved remarkable performance in continuous control tasks, yet their computational complexity is often incompatible…
Function Based Isolation Forest (FuBIF): A Unifying Framework for Interpretable Isolation-Based Anomaly Detection
Alessio Arcudi, Alessandro Ferreri, Francesco Borsatti +1
Anomaly Detection (AD) is evolving through algorithms capable of identifying outliers in complex datasets. The Isolation Forest (IF), a pivotal AD technique, exhibits adaptability…
ProDER: A Continual Learning Approach for Fault Classification and Localization in Evolving Smart Grids
Emad Efatinasab, Nahal Azadi, Davide Dalle Pezze +3
Data-driven fault diagnosis models for smart grids are usually trained once on a fixed dataset, whereas in operation new fault types appear and monitoring is extended to new grid z…
Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning
Alessio Arcudi, Davide Sartor, Alberto Sinigaglia +2
This paper introduces MANGO (Multilayer Abstraction for Nested Generation of Options), a novel hierarchical reinforcement learning framework designed to address the challenges of l…
Explainable Anomaly Detection for Electric Vehicles Charging Stations
Matteo Cederle, Andrea Mazzucco, Andrea Demartini +4
Electric vehicles (EV) charging stations are one of the critical infrastructures needed to support the transition to renewable-energy-based mobility, but ensuring their reliability…
Towards Explainable Anomaly Detection in Shared Mobility Systems
Elnur Isgandarov, Matteo Cederle, Federico Chiariotti +1
Shared mobility systems, such as bike-sharing networks, play a crucial role in urban transportation. Identifying anomalies in these systems is essential for optimizing operations,…
Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond
Marina Ceccon, Giandomenico Cornacchia, Davide Dalle Pezze +2
Undesirable biases encoded in the data are key drivers of algorithmic discrimination. Their importance is widely recognized in the algorithmic fairness literature, as well as legis…
Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations
Davide Sartor, Alberto Sinigaglia, Gian Antonio Susto
Conventional techniques for imposing monotonicity in MLPs by construction involve the use of non-negative weight constraints and bounded activation functions, which pose well-known…
Simple and Effective Specialized Representations for Fair Classifiers
Alberto Sinigaglia, Davide Sartor, Marina Ceccon +1
Fair classification is a critical challenge that has gained increasing importance due to international regulations and its growing use in high-stakes decision-making settings. Exis…
Continual Learning for Behavior-based Driver Identification
Mattia Fanan, Davide Dalle Pezze, Emad Efatinasab +3
Behavior-based Driver Identification is an emerging technology that recognizes drivers based on their unique driving behaviors, offering important applications such as vehicle thef…
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…
Exploiting Estimation Bias in Clipped Double Q-Learning for Continous Control Reinforcement Learning Tasks
Niccolò Turcato, Alberto Sinigaglia, Alberto Dalla Libera +2
Continuous control Deep Reinforcement Learning (RL) approaches are known to suffer from estimation biases, leading to suboptimal policies. This paper introduces innovative methods…