Search Results for author:
Found 4 papers, 2 papers with code
Active Reward Learning from Online Preferences
Robot policies need to adapt to human preferences and/or new environments.
Bayesian Meta-Learning Through Variational Gaussian Processes
This problem setting can be extended to the Bayesian context, wherein rather than predicting a single label for each query data point, a model predicts a distribution of labels capturing its uncertainty.
Learning Multimodal Rewards from Rankings
However, expert feedback is often assumed to be drawn from an underlying unimodal reward function.
A Hierarchical Approach to Scaling Batch Active Search Over Structured Data
As modern data sets grow, so does the need to scale active search to large data sets and batch sizes.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
