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Found 5 papers, 3 papers with code
Challenges in Training PINNs: A Loss Landscape Perspective
This paper explores challenges in training Physics-Informed Neural Networks (PINNs), emphasizing the role of the loss landscape in the training process.
PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates
This paper introduces PROMISE ($\textbf{Pr}$econditioned Stochastic $\textbf{O}$ptimization $\textbf{M}$ethods by $\textbf{I}$ncorporating $\textbf{S}$calable Curvature $\textbf{E}$stimates), a suite of sketching-based preconditioned stochastic gradient algorithms for solving large-scale convex optimization problems arising in machine learning.
Robust, randomized preconditioning for kernel ridge regression
This paper introduces two randomized preconditioning techniques for robustly solving kernel ridge regression (KRR) problems with a medium to large number of data points ($10^4 \leq N \leq 10^7$).
SketchySGD: Reliable Stochastic Optimization via Randomized Curvature Estimates
Numerical experiments on both ridge and logistic regression problems with dense and sparse data, show that SketchySGD equipped with its default hyperparameters can achieve comparable or better results than popular stochastic gradient methods, even when they have been tuned to yield their best performance.
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression
In the present paper, we show that, in the case of ridge regression, the CV loss may fail to be quasiconvex and thus may have multiple local optima.
