65 citations · 78 across the 7 of their papers we have counts for
9 papers
Temporal Predictive Multiplicity: Equally Accurate Time Series Models Yield Different Forecast Trajectories
Emanuele Albini, Francesca Toni, Saumitra Mishra +1
Models with near-identical predictive performance can yield substantially different predictions, a phenomenon known as predictive multiplicity. Prior work has mostly studied this a…
Entropic Projection Alignment: Estimating, Explaining, and Improving Model Performance Under Distribution Shift
Salim I. Amoukou, Emanuele Albini, Tom Bewley +2
We propose a unified framework for addressing three key challenges of distribution shift: (1) estimating a model's performance on an unlabeled target domain, (2) explaining the shi…
ShapShift: Explaining Model Prediction Shifts with Subgroup Conditional Shapley Values
Tom Bewley, Salim I. Amoukou, Emanuele Albini +2
Changes in input distribution can induce shifts in the average predictions of machine learning models. Such prediction shifts may impact downstream business outcomes (e.g. a bank's…
REFRESH: Responsible and Efficient Feature Reselection Guided by SHAP Values
Shubham Sharma, Sanghamitra Dutta, Emanuele Albini +3
Feature selection is a crucial step in building machine learning models. This process is often achieved with accuracy as an objective, and can be cumbersome and computationally exp…
On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations
Emanuele Albini, Shubham Sharma, Saumitra Mishra +2
Explainable Artificial Intelligence (XAI) has received widespread interest in recent years, and two of the most popular types of explanations are feature attributions, and counterf…
Counterfactual Shapley Additive Explanations
Emanuele Albini, Jason Long, Danial Dervovic +1
Feature attributions are a common paradigm for model explanations due to their simplicity in assigning a single numeric score for each input feature to a model. In the actionable r…
Argumentative XAI: A Survey
Kristijonas Čyras, Antonio Rago, Emanuele Albini +2
Explainable AI (XAI) has been investigated for decades and, together with AI itself, has witnessed unprecedented growth in recent years. Among various approaches to XAI, argumentat…
Influence-Driven Explanations for Bayesian Network Classifiers
Antonio Rago, Emanuele Albini, Pietro Baroni +1
One of the most pressing issues in AI in recent years has been the need to address the lack of explainability of many of its models. We focus on explanations for discrete Bayesian…
Deep Argumentative Explanations
Emanuele Albini, Piyawat Lertvittayakumjorn, Antonio Rago +1
Despite the recent, widespread focus on eXplainable AI (XAI), explanations computed by XAI methods tend to provide little insight into the functioning of Neural Networks (NNs). We…