2 citations · 4 across the 17 of their papers we have counts for
22 papers
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…
LLM Alignment--Utility Asymmetry under Semantic-Preserving Transformations
Mohan Li, Chengyu Yu, Francesco Sovrano +2
Large Language Model (LLM) alignment is intended to ensure that models remain helpful and safe, but its stability under input distributional shift is not yet fully understood. Prio…
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…
Concept-based Visual Counterfactual Explanations with Diffusion Models
Yassine Oueslati, Daniil Kirilenko, Martin Gjoreski +1
Visual counterfactual explanations aim to answer "what minimal change to this image would flip the model's prediction?", and are increasingly important as vision models are deploye…
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…
A foundation model for electrodermal activity data
Leonardo Alchieri, Matteo Garzon, Lidia Alecci +4
Foundation models have recently extended beyond natural language and vision to timeseries domains, including physiological signals. However, progress in electrodermal activity (EDA…
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…
Towards Transparent Reasoning: What Drives Faithfulness in Large Language Models?
Teague McMillan, Gabriele Dominici, Martin Gjoreski +1
Large Language Models (LLMs) often produce explanations that do not faithfully reflect the factors driving their predictions. In healthcare settings, such unfaithfulness is especia…
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…
Evaluating Explanations Through LLMs: Beyond Traditional User Studies
Francesco Bombassei De Bona, Gabriele Dominici, Tim Miller +2
As AI becomes fundamental in sectors like healthcare, explainable AI (XAI) tools are essential for trust and transparency. However, traditional user studies used to evaluate these…
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…
AnyCBMs: How to Turn Any Black Box into a Concept Bottleneck Model
Gabriele Dominici, Pietro Barbiero, Francesco Giannini +2
Interpretable deep learning aims at developing neural architectures whose decision-making processes could be understood by their users. Among these techniqes, Concept Bottleneck Mo…
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…