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A Generalized Acquisition Function for Preference-based Reward Learning
Preference-based reward learning is a popular technique for teaching robots and autonomous systems how a human user wants them to perform a task.
The Effect of Modeling Human Rationality Level on Learning Rewards from Multiple Feedback Types
In this work, we advocate that grounding the rationality coefficient in real data for each feedback type, rather than assuming a default value, has a significant positive effect on reward learning.
Multi-Modal Prototype Learning for Interpretable Multivariable Time Series Classification
We validate our framework on a simulated dataset with embedded patterns, as well as a real human activity recognition problem.
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