4 citations · 9 across the 5 of their papers we have counts for
6 papers
Why not both? Complementing explanations with uncertainty, and the role of self-confidence in Human-AI collaboration
Ioannis Papantonis, Vaishak Belle
AI and ML models have already found many applications in critical domains, such as healthcare and criminal justice. However, fully automating such high-stakes applications can rais…
Explainability in Machine Learning: a Pedagogical Perspective
Andreas Bueff, Ioannis Papantonis, Auste Simkute +1
Given the importance of integrating of explainability into machine learning, at present, there are a lack of pedagogical resources exploring this. Specifically, we have found a nee…
Principled Diverse Counterfactuals in Multilinear Models
Ioannis Papantonis, Vaishak Belle
Machine learning (ML) applications have automated numerous real-life tasks, improving both private and public life. However, the black-box nature of many state-of-the-art models po…
Principles and Practice of Explainable Machine Learning
Vaishak Belle, Ioannis Papantonis
Artificial intelligence (AI) provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data in an automated manner is a…
On Constraint Definability in Tractable Probabilistic Models
Ioannis Papantonis, Vaishak Belle
Incorporating constraints is a major concern in probabilistic machine learning. A wide variety of problems require predictions to be integrated with reasoning about constraints, fr…
Interventions and Counterfactuals in Tractable Probabilistic Models: Limitations of Contemporary Transformations
Ioannis Papantonis, Vaishak Belle
In recent years, there has been an increasing interest in studying causality-related properties in machine learning models generally, and in generative models in particular. While…