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Leveraging Contextual Counterfactuals Toward Belief Calibration
Beliefs and values are increasingly being incorporated into our AI systems through alignment processes, such as carefully curating data collection principles or regularizing the loss function used for training.
Reasoning about Counterfactuals to Improve Human Inverse Reinforcement Learning
We propose to incorporate the learner's current understanding of the robot's decision making into our model of human IRL, so that a robot can select demonstrations that maximize the human's understanding.
Requirements for Developing Robust Neural Networks
Validation accuracy is a necessary, but not sufficient, measure of a neural network classifier's quality.
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