Negative-capable ridge regression uses controlled negative regularization as anti-shrinkage to increase effective complexity along weak eigendirections and mitigate underfitting in small-data regression.
On large-batch training for deep learning: Generalization gap and sharp minima
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C3PO is a foundation model for bilevel pricing optimization that trains on simulated discrete choice data and retrieves elasticity priors from literature to improve revenue KPIs under business constraints.
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A Ridge Too Far: Correcting Over-Shrinkage via Negative Regularization
Negative-capable ridge regression uses controlled negative regularization as anti-shrinkage to increase effective complexity along weak eigendirections and mitigate underfitting in small-data regression.
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Causal-Aware Foundation-Model for Bilevel Optimization in Discrete Choice Settings
C3PO is a foundation model for bilevel pricing optimization that trains on simulated discrete choice data and retrieves elasticity priors from literature to improve revenue KPIs under business constraints.
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