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08/30/2023
Iterative Reward Shaping using Human Feedback for Correcting Reward Misspecification
A well-defined reward function is crucial for successful training of an ...
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06/23/2023
Co-creating a globally interpretable model with human input
We consider an aggregated human-AI collaboration aimed at generating a j...
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06/10/2023
Interpretable Differencing of Machine Learning Models
Understanding the differences between machine learning (ML) models is of...
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02/19/2023
AutoDOViz: Human-Centered Automation for Decision Optimization
We present AutoDOViz, an interactive user interface for automated decisi...
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11/02/2022
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach
Interpretable and explainable machine learning has seen a recent surge o...
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07/18/2022
Boolean Decision Rules for Reinforcement Learning Policy Summarisation
Explainability of Reinforcement Learning (RL) policies remains a challen...
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03/28/2022
User Driven Model Adjustment via Boolean Rule Explanations
AI solutions are heavily dependant on the quality and accuracy of the in...
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01/04/2022
FROTE: Feedback Rule-Driven Oversampling for Editing Models
Machine learning models may involve decision boundaries that change over...
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12/17/2021
Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents
In complex tasks where the reward function is not straightforward and co...
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10/24/2021
Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels
The confusion matrix, a ubiquitous visualization for helping people eval...
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06/25/2020
Perfusion Quantification from Endoscopic Videos: Learning to Read Tumor Signatures
Intra-operative identification of malignant versus benign or healthy tis...
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02/11/2018
The Use of Presence Data in Modelling Demand for Transportation
We consider the applicability of the data from operators of cellular sys...
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12/13/2015