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Found 6 papers, 1 papers with code
Maximum Likelihood Estimation for Hawkes Processes with self-excitation or inhibition
In this paper, we present a maximum likelihood method for estimating the parameters of a univariate Hawkes process with self-excitation or inhibition.
Approximating Lipschitz continuous functions with GroupSort neural networks
Recent advances in adversarial attacks and Wasserstein GANs have advocated for use of neural networks with restricted Lipschitz constants.
Some Theoretical Insights into Wasserstein GANs
Generative Adversarial Networks (GANs) have been successful in producing outstanding results in areas as diverse as image, video, and text generation.
Proximal boosting: aggregating weak learners to minimize non-differentiable losses
Gradient boosting is a prediction method that iteratively combines weak learners to produce a complex and accurate model.
Infinite-Task Learning with RKHSs
A step further consists of learning a continuum of tasks for various loss functions.
Joint quantile regression in vector-valued RKHSs
Addressing the will to give a more complete picture than an average relationship provided by standard regression, a novel framework for estimating and predicting simultaneously several conditional quantiles is introduced.
