1,782 research outputs found
Teaching Inverse Reinforcement Learners via Features and Demonstrations
Learning near-optimal behaviour from an expert's demonstrations typically
relies on the assumption that the learner knows the features that the true
reward function depends on. In this paper, we study the problem of learning
from demonstrations in the setting where this is not the case, i.e., where
there is a mismatch between the worldviews of the learner and the expert. We
introduce a natural quantity, the teaching risk, which measures the potential
suboptimality of policies that look optimal to the learner in this setting. We
show that bounds on the teaching risk guarantee that the learner is able to
find a near-optimal policy using standard algorithms based on inverse
reinforcement learning. Based on these findings, we suggest a teaching scheme
in which the expert can decrease the teaching risk by updating the learner's
worldview, and thus ultimately enable her to find a near-optimal policy.Comment: NeurIPS'2018 (extended version
Essential guidelines for computational method benchmarking
In computational biology and other sciences, researchers are frequently faced
with a choice between several computational methods for performing data
analyses. Benchmarking studies aim to rigorously compare the performance of
different methods using well-characterized benchmark datasets, to determine the
strengths of each method or to provide recommendations regarding suitable
choices of methods for an analysis. However, benchmarking studies must be
carefully designed and implemented to provide accurate, unbiased, and
informative results. Here, we summarize key practical guidelines and
recommendations for performing high-quality benchmarking analyses, based on our
experiences in computational biology.Comment: Minor update
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