A novel adaptive multi-task learning framework with projection-penalized PCA learns cross-sector factor subspace relatedness to improve multi-sector factor model estimation and portfolio optimization.
arXiv preprint arXiv:2005.00944 , year=
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iGSP uses implicit gradient subspace projection in two phases to enable efficient continual adaptation of vision-language models, claiming SOTA accuracy with 42.7% fewer trainable parameters and 86.9% less total parameter growth.
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Adaptive Multi-task Learning for Multi-sector Portfolio Optimization
A novel adaptive multi-task learning framework with projection-penalized PCA learns cross-sector factor subspace relatedness to improve multi-sector factor model estimation and portfolio optimization.
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iGSP:Implicit Gradient Subspace Projection for Efficient Continual Learning of Vision-Language Models
iGSP uses implicit gradient subspace projection in two phases to enable efficient continual adaptation of vision-language models, claiming SOTA accuracy with 42.7% fewer trainable parameters and 86.9% less total parameter growth.
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