10 citations · 71 across the 49 of their papers we have counts for
5 papers · 1 filter
Using Abstraction for Interpretable Robot Programs in Stochastic Domains
Till Hofmann, Vaishak Belle
A robot's actions are inherently stochastic, as its sensors are noisy and its actions do not always have the intended effects. For this reason, the agent language Golog has been ex…
Abstracting Noisy Robot Programs
Till Hofmann, Vaishak Belle
Abstraction is a commonly used process to represent some low-level system by a more coarse specification with the goal to omit unnecessary details while preserving important aspect…
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…
Vision Checklist: Towards Testable Error Analysis of Image Models to Help System Designers Interrogate Model Capabilities
Xin Du, Benedicte Legastelois, Bhargavi Ganesh +5
Using large pre-trained models for image recognition tasks is becoming increasingly common owing to the well acknowledged success of recent models like vision transformers and othe…
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…