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cs.LG2026
Joint Surrogate Learning of Objectives, Constraints, and Sensitivities for Efficient Multi-objective Optimization of Neural Dynamical Systems
Frithjof Gressmann, Ivan Georgiev Raikov, Seung Hyun Kim +3
Gaussian process surrogates dominate constrained multi-objective optimization because they are effective in data-scarce regimes, but their cubic scaling in training samples limits…
cs.LG2020★ 6 cited
Improving Neural Network Training in Low Dimensional Random Bases
Frithjof Gressmann, Zach Eaton-Rosen, Carlo Luschi
Stochastic Gradient Descent (SGD) has proven to be remarkably effective in optimizing deep neural networks that employ ever-larger numbers of parameters. Yet, improving the efficie…