5 citations · 10 across the 7 of their papers we have counts for
8 papers
JUCAL: Jointly Calibrating Aleatoric and Epistemic Uncertainty in Classification Tasks
Jakob Heiss, Sören Lambrecht, Jakob Weissteiner +4
We study post-calibration uncertainty for trained ensembles of classifiers. Specifically, we consider both aleatoric (label noise) and epistemic (model) uncertainty. Among the most…
Robust Utility Optimization via a GAN Approach
Florian Krach, Josef Teichmann, Hanna Wutte
Robust utility optimization enables an investor to deal with market uncertainty in a structured way, with the goal of maximizing the worst-case outcome. In this work, we propose a…
Machine Learning-powered Pricing of the Multidimensional Passport Option
Josef Teichmann, Hanna Wutte
Introduced in the late 90s, the passport option gives its holder the right to trade in a market and receive any positive gain in the resulting traded account at maturity. Pricing t…
How (Implicit) Regularization of ReLU Neural Networks Characterizes the Learned Function -- Part II: the Multi-D Case of Two Layers with Random First Layer
Jakob Heiss, Josef Teichmann, Hanna Wutte
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 ne…
How Infinitely Wide Neural Networks Can Benefit from Multi-task Learning -- an Exact Macroscopic Characterization
Jakob Heiss, Josef Teichmann, Hanna Wutte
In practice, multi-task learning (through learning features shared among tasks) is an essential property of deep neural networks (NNs). While infinite-width limits of NNs can provi…
A deep learning model for gas storage optimization
Nicolas Curin, Michael Kettler, Xi Kleisinger-Yu +4
To the best of our knowledge, the application of deep learning in the field of quantitative risk management is still a relatively recent phenomenon. In this article, we utilize tec…
NOMU: Neural Optimization-based Model Uncertainty
Jakob Heiss, Jakob Weissteiner, Hanna Wutte +2
We study methods for estimating model uncertainty for neural networks (NNs) in regression. To isolate the effect of model uncertainty, we focus on a noiseless setting with scarce t…
How Implicit Regularization of ReLU Neural Networks Characterizes the Learned Function -- Part I: the 1-D Case of Two Layers with Random First Layer
Jakob Heiss, Josef Teichmann, Hanna Wutte
In this paper, we consider one dimensional (shallow) ReLU neural networks in which weights are chosen randomly and only the terminal layer is trained. First, we mathematically show…