2 citations · 2 across the 9 of their papers we have counts for
7 papers · 1 filter
A CaLLM Judge is a Better Judge: Mitigating Bias in LLM-as-a-judge
Cesare Barbera, Luca Stradiotti, Lorenzo Cascioli +6
Large language models (LLMs) are increasingly used as judges to predict which of two candidate answers a human evaluator would prefer, providing a scalable alternative when human e…
Should I stay or should I show? Learning to selectively disclose information
Carlotta Giacchetta, Alessando Bogani, Cesare Barbera +4
In many high-stakes settings, human decision-makers can acquire support information before making a decision. However, acquiring information is costly, and disclosure may fail to i…
Too Much of the Same: From Algorithmic to Human Bias in Learning to Defer
Dario Pesenti, Alessandro Bogani, Stefano Teso +1
Learning to Defer (LtD) extends supervised learning by allowing a Machine Learning (ML) model to defer harder or less confident decisions to a human expert. Despite being geared fo…
Are Concept Bottleneck Models Effective as Decision-Support Systems?
Alessandro Bogani, Nicola Debole, Emanuele Marconato +3
Concept Bottleneck Models (CBMs) are interpretable-by-design neural networks that detect human-understandable concepts from the input and use them to generate predictions. By allow…
Divide et Calibra: Multiclass Local Calibration via Vector Quantization
Cesare Barbera, Lorenzo Perini, Giovanni De Toni +2
Accurate and well-calibrated Machine Learning (ML) models are mandatory in high-stakes settings, yet effective multiclass calibration remains challenging: global approaches assume…
Concepts Worth Having: Refining VLM-Guided Concept Bottleneck Models with Minimal Annotations
Nicola Debole, Andrea Passerini, Stefano Teso +2
Concept-bottleneck models (CBMs) are neural classifiers that compute predictions from high-level concepts extracted from the input. CBMs ensure stakeholders can understand the conc…
Concise and Logically Consistent Conformal Sets for Neuro-Symbolic Concept-Based Models
Samuele Bortolotti, Emanuele Marconato, Andrea Pugnana +2
Neuro-Symbolic Concept-based Models (NeSy-CBMs) are a family of architectures that integrate neural networks with symbolic reasoning for enhanced reliability in high-stakes applica…