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09/12/2023
Certified Robust Models with Slack Control and Large Lipschitz Constants
Despite recent success, state-of-the-art learning-based models remain hi...
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08/21/2023
Unlocking Accuracy and Fairness in Differentially Private Image Classification
Privacy-preserving machine learning aims to train models on private data...
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07/18/2023
Conformal prediction under ambiguous ground truth
In safety-critical classification tasks, conformal prediction allows to ...
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07/05/2023
Evaluating AI systems under uncertain ground truth: a case study in dermatology
For safety, AI systems in health undergo thorough evaluations before dep...
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03/20/2023
Robustifying Token Attention for Vision Transformers
Despite the success of vision transformers (ViTs), they still suffer fro...
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04/26/2022
On Fragile Features and Batch Normalization in Adversarial Training
Modern deep learning architecture utilize batch normalization (BN) to st...
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01/30/2022
Improving Corruption and Adversarial Robustness by Enhancing Weak Subnets
Deep neural networks have achieved great success in many computer vision...
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10/18/2021
Learning Optimal Conformal Classifiers
Modern deep learning based classifiers show very high accuracy on test d...
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07/12/2021
A Closer Look at the Adversarial Robustness of Information Bottleneck Models
We study the adversarial robustness of information bottleneck models for...
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04/16/2021
Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators
Deep neural network (DNN) accelerators received considerable attention i...
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04/09/2021
Relating Adversarially Robust Generalization to Flat Minima
Adversarial training (AT) has become the de-facto standard to obtain mod...
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06/24/2020
Bit Error Robustness for Energy-Efficient DNN Accelerators
Deep neural network (DNN) accelerators received considerable attention i...
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05/05/2020
Adversarial Training against Location-Optimized Adversarial Patches
Deep neural networks have been shown to be susceptible to adversarial ex...
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10/14/2019
Confidence-Calibrated Adversarial Training: Towards Robust Models Generalizing Beyond the Attack Used During Training
Adversarial training is the standard to train models robust against adve...
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12/03/2018
Disentangling Adversarial Robustness and Generalization
Obtaining deep networks that are robust against adversarial examples and...
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05/18/2018
Learning 3D Shape Completion under Weak Supervision
We address the problem of 3D shape completion from sparse and noisy poin...
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12/06/2016