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Using Abstraction for Interpretable Robot Programs in Stochastic Domains


Jul 26, 2022
Till Hofmann, Vaishak Belle

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* Presented at the KR'22 Workshop on Explainable Logic-Based Knowledge Representation (XLoKR) 

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Abstracting Noisy Robot Programs


Apr 07, 2022
Till Hofmann, Vaishak Belle

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Explainability in Machine Learning: a Pedagogical Perspective


Feb 21, 2022
Andreas Bueff, Ioannis Papantonis, Auste Simkute, Vaishak Belle

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Vision Checklist: Towards Testable Error Analysis of Image Models to Help System Designers Interrogate Model Capabilities


Jan 31, 2022
Xin Du, Benedicte Legastelois, Bhargavi Ganesh, Ajitha Rajan, Hana Chockler, Vaishak Belle, Stuart Anderson, Subramanian Ramamoorthy

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* 17 pages, 18 figures 

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Principled Diverse Counterfactuals in Multilinear Models


Jan 17, 2022
Ioannis Papantonis, Vaishak Belle

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MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks


Nov 02, 2021
Nicholas Hoernle, Rafael Michael Karampatsis, Vaishak Belle, Kobi Gal

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* Submitted to AAAI2022 

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Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief


Oct 06, 2021
Christian Muise, Vaishak Belle, Paolo Felli, Sheila McIlraith, Tim Miller, Adrian R. Pearce, Liz Sonenberg

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* Published in Special Issue of the Artificial Intelligence Journal (AIJ) on Epistemic Planning 

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Learning Implicitly with Noisy Data in Linear Arithmetic


Oct 23, 2020
Alexander Philipp Rader, Ionela G. Mocanu, Vaishak Belle, Brendan Juba

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Principles and Practice of Explainable Machine Learning


Sep 18, 2020
Vaishak Belle, Ioannis Papantonis

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