101 citations · 105 across the 19 of their papers we have counts for
18 papers · 1 filter
Causal Chain of Thought: Mitigating Attention Leakage via Context Isolation
Gabriele Dominici, Teague McMillan, Fazl Barez +2
Chain-of-thought (CoT) prompting elicits step-by-step rationales that appear to explain model predictions, but there is no guarantee these steps causally drive the final answer. In…
TrustmeWatcher: An Application for Workplace Micro-Sensing and Explainable Well-Being Feedback
Chengyu Yu, Leon Jacopo Costa, Zoja Anžur +6
Workplace sensing studies combine long-running behaviour traces with self-reports, yet the tools that collect those data often sit apart from the interface that returns results. We…
Distributed Subliminal Learning: Replacing Model Updates with Random-Carrier Outputs
Dario Fenoglio, Gabriele Dominici, Martin Gjoreski +1
Collaborative learning typically exchanges model parameters: federated clients communicate updates, while independently adapted foundation models are combined by exchanging adapter…
SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention
Lidia Losavio, Francesco Sovrano, Dario Fenoglio +2
Money laundering threatens financial stability and exposes institutions to penalties, motivating automated detection. Because laundering schemes often emerge through relational pat…
Federated Learning with Energy-Based Structured Probabilistic Inference
Dario Fenoglio, Daniil Kirilenko, Martin Gjoreski +1
Federated Learning (FL) enables multiple clients to collaboratively train machine learning models without sharing their local data. However, heterogeneity in client data and the pr…
Neuron-Anchored Rule Extraction for Large Language Models via Contrastive Hierarchical Ablation
Francesco Sovrano, Gabriele Dominici, Marc Langheinrich
A central goal of explainable AI is to express large language model (LLM) decision logic symbolically and ground it in internal mechanisms. Existing rule-extraction methods usually…
In-Context Symbolic Regression for Robustness-Improved Kolmogorov-Arnold Networks
Francesco Sovrano, Lidia Losavio, Giulia Vilone +1
Symbolic regression aims to replace black-box predictors with concise analytical expressions that can be inspected and validated in scientific machine learning. Kolmogorov-Arnold N…
Federated Learning with Profile Mapping under Distribution Shifts and Drifts
Mohan Li, Dario Fenoglio, Martin Gjoreski +1
Federated Learning (FL) enables decentralized model training across clients without sharing raw data, but its performance degrades under real-world data heterogeneity. Existing met…
ERIS: Enhancing the Privacy-Utility-Scalability Trade-Off in Federated Learning via Federated Shard Aggregation
Dario Fenoglio, Pasquale Polverino, Jacopo Quizi +3
Scaling Federated Learning (FL) to billion-parameter models forces a challenging trade-off between privacy, scalability, and model utility. Existing solutions often tackle these ch…
Federated Concept-Based Models: Interpretable models with distributed supervision
Dario Fenoglio, Arianna Casanova, Francesco De Santis +6
Concept-based Models (CMs) enhance interpretability in deep learning by grounding predictions in human-understandable concepts. However, concept annotations are costly and rarely a…
Multi-Frequency Federated Learning for Human Activity Recognition Using Head-Worn Sensors
Dario Fenoglio, Mohan Li, Davide Casnici +5
Human Activity Recognition (HAR) benefits various application domains, including health and elderly care. Traditional HAR involves constructing pipelines reliant on centralized use…
FLUX: Efficient Descriptor-Driven Clustered Federated Learning under Arbitrary Distribution Shifts
Dario Fenoglio, Mohan Li, Pietro Barbiero +3
Federated Learning (FL) enables collaborative model training across multiple clients while preserving data privacy. Traditional FL methods often use a global model to fit all clien…
A Survey on Federated Learning in Human Sensing
Mohan Li, Martin Gjoreski, Pietro Barbiero +4
Human Sensing, a field that leverages technology to monitor human activities, psycho-physiological states, and interactions with the environment, enhances our understanding of huma…
Counterfactual Explanations for Clustering Models
Aurora Spagnol, Kacper Sokol, Pietro Barbiero +2
Clustering algorithms rely on complex optimisation processes that may be difficult to comprehend, especially for individuals who lack technical expertise. While many explainable ar…
Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
Gabriele Dominici, Pietro Barbiero, Mateo Espinosa Zarlenga +4
Causal opacity denotes the difficulty in understanding the "hidden" causal structure underlying the decisions of deep neural network (DNN) models. This leads to the inability to re…
Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
Dario Fenoglio, Gabriele Dominici, Pietro Barbiero +3
Federated Learning (FL), a privacy-aware approach in distributed deep learning environments, enables many clients to collaboratively train a model without sharing sensitive data, t…
Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability
Fatima Ezzeddine, Mirna Saad, Omran Ayoub +5
Anomaly detection (AD), also referred to as outlier detection, is a statistical process aimed at identifying observations within a dataset that significantly deviate from the expec…
Counterfactual Concept Bottleneck Models
Gabriele Dominici, Pietro Barbiero, Francesco Giannini +3
Current deep learning models are not designed to simultaneously address three fundamental questions: predict class labels to solve a given classification task (the "What?"), simula…