Klaus‐Robert Müller
5 papers in the PaperMetrix corpus
Papers by this author
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Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution
2019
Developing techniques for adversarial attack and defense is an important research field for establishing reliable machine learning and its applications. Many existing methods employ Gaussian random variables for exploring the data space to find the …
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Learning domain invariant representations by joint Wasserstein distance minimization
2023 · Neural Networks
Domain shifts in the training data are common in practical applications of machine learning; they occur for instance when the data is coming from different sources. Ideally, a ML model should work well independently of …
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Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations
2022 · arXiv (Cornell University)
While the evaluation of explanations is an important step towards trustworthy models, it needs to be done carefully, and the employed metrics need to be well-understood. Specifically model randomization testing is often overestimated and regarded …
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Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces
2022 · arXiv (Cornell University)
Explainable AI aims to overcome the black-box nature of complex ML models like neural networks by generating explanations for their predictions. Explanations often take the form of a heatmap identifying input features (e.g. pixels) that …
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Physics-Informed Bayesian Optimization of Variational Quantum Circuits
2024 · arXiv (Cornell University)
In this paper, we propose a novel and powerful method to harness Bayesian optimization for Variational Quantum Eigensolvers (VQEs) -- a hybrid quantum-classical protocol used to approximate the ground state of a quantum Hamiltonian. Specifically, …