Alexander Ziller

Orcid: 0000-0002-3242-0195

According to our database1, Alexander Ziller authored at least 28 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Other 

Links

On csauthors.net:

Bibliography

2024
Bounding Reconstruction Attack Success of Adversaries Without Data Priors.
CoRR, 2024

2023
Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging.
CoRR, 2023

How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection.
CoRR, 2023

Bias-Aware Minimisation: Understanding and Mitigating Estimator Bias in Private SGD.
CoRR, 2023

Explainable 2D Vision Models for 3D Medical Data.
CoRR, 2023

Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy.
CoRR, 2023

Private, fair and accurate: Training large-scale, privacy-preserving AI models in radiology.
CoRR, 2023

Optimal privacy guarantees for a relaxed threat model: Addressing sub-optimal adversaries in differentially private machine learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Body Fat Estimation from Surface Meshes Using Graph Neural Networks.
Proceedings of the Shape in Medical Imaging - International Workshop, 2023

Exploiting Segmentation Labels and Representation Learning to Forecast Therapy Response of PDAC Patients.
Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023

2022
Unified Interpretation of the Gaussian Mechanism for Differential Privacy Through the Sensitivity Index.
J. Priv. Confidentiality, 2022

Privacy: An Axiomatic Approach.
Entropy, 2022

How Do Input Attributes Impact the Privacy Loss in Differential Privacy?
CoRR, 2022

Generalised Likelihood Ratio Testing Adversaries through the Differential Privacy Lens.
CoRR, 2022

SmoothNets: Optimizing CNN architecture design for differentially private deep learning.
CoRR, 2022

Differentially private training of residual networks with scale normalisation.
CoRR, 2022

2021
Adversarial interference and its mitigations in privacy-preserving collaborative machine learning.
Nat. Mach. Intell., 2021

End-to-end privacy preserving deep learning on multi-institutional medical imaging.
Nat. Mach. Intell., 2021

Distributed Machine Learning and the Semblance of Trust.
CoRR, 2021

Complex-valued deep learning with differential privacy.
CoRR, 2021

Partial sensitivity analysis in differential privacy.
CoRR, 2021

An automatic differentiation system for the age of differential privacy.
CoRR, 2021

Differentially private training of neural networks with Langevin dynamics forcalibrated predictive uncertainty.
CoRR, 2021

Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation.
CoRR, 2021

Differentially private federated deep learning for multi-site medical image segmentation.
CoRR, 2021

2020
Privacy-preserving medical image analysis.
CoRR, 2020

2019
Oktoberfest Food Dataset.
CoRR, 2019

2015
Learning games for configuration and diagnosis tasks.
Proceedings of the 17th International Configuration Workshop, 2015


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