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Found 7 papers, 5 papers with code
Robust Utility Optimization via a GAN Approach
Lastly, we uncover that our generative approach for learning optimal, (non-) robust investments under trading costs generates universally applicable alternatives to well known asymptotic strategies of idealized settings.
Machine Learning-powered Pricing of the Multidimensional Passport Option
These approaches prove to be successful for pricing the passport option in one-dimensional and multi-dimensional uncorrelated BS markets.
How (Implicit) Regularization of ReLU Neural Networks Characterizes the Learned Function -- Part II: the Multi-D Case of Two Layers with Random First Layer
Randomized neural networks (randomized NNs), where only the terminal layer's weights are optimized constitute a powerful model class to reduce computational time in training the neural network model.
How Infinitely Wide Neural Networks Can Benefit from Multi-task Learning -- an Exact Macroscopic Characterization
In practice, multi-task learning (through learning features shared among tasks) is an essential property of deep neural networks (NNs).
NOMU: Neural Optimization-based Model Uncertainty
To isolate the effect of model uncertainty, we focus on a noiseless setting with scarce training data.
A deep learning model for gas storage optimization
To the best of our knowledge, the application of deep learning in the field of quantitative risk management is still a relatively recent phenomenon.
How Implicit Regularization of ReLU Neural Networks Characterizes the Learned Function -- Part I: the 1-D Case of Two Layers with Random First Layer
In this paper, we consider one dimensional (shallow) ReLU neural networks in which weights are chosen randomly and only the terminal layer is trained.
