CARLOS employs an aggregate deep neural network trained on progressively finer time grids with adaptive sampling to learn continuous-time exercise boundaries for optimal stopping, delivering higher values than discrete Bermudan methods.
Titsias, Jonathan Schwarz, Alexander G
2 Pith papers cite this work. Polarity classification is still indexing.
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A plug-and-play KL regularizer that masks the target token and renormalizes probabilities to improve the learning-forgetting trade-off in LoRA adaptation of LLMs.
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Mask the Target: A Plug-and-Play Regularizer Against LoRA Forgetting
A plug-and-play KL regularizer that masks the target token and renormalizes probabilities to improve the learning-forgetting trade-off in LoRA adaptation of LLMs.