53 citations · 130 across the 6 of their papers we have counts for
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cs.LG2023★ 1 cited
Adaptive Batch Sizes for Active Learning A Probabilistic Numerics Approach
Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen +4
Active learning parallelization is widely used, but typically relies on fixing the batch size throughout experimentation. This fixed approach is inefficient because of a dynamic tr…
cs.LG2022★ 2 cited
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations
Tim G. J. Rudner, Cong Lu, Michael A. Osborne +2
KL-regularized reinforcement learning from expert demonstrations has proved successful in improving the sample efficiency of deep reinforcement learning algorithms, allowing them t…