On the adversarial robustness of Gaussian processes by Patanè, A

Calibrating the classifier: siamese neural network architecture for end-to-end arousal recognition from ECG by Patane, A, Kwiatkowska, M

Closed-loop quantitative verification of rate-adaptive pacemakers by Paoletti, N, Patane, A, Kwiatkowska, M

Gaussian processes with physiologically-inspired priors for physical arousal recognition by Ghiasi, S, Patane, A, Patane, A, Greco, A, Greco, A, Laurenti, L, Scilingo, EP, Scilingo, EP, Kwiatkowska, M

Semantic place understanding for human–robot coexistence—toward intelligent workplaces by Rosa, S, Patanè, A, Lu, C, Trigoni, N

Automated recognition of sleep arousal using multimodal and personalized deep ensembles of neural networks by Patane, A, Ghiasi, S, Scilingo, E, Kwiatkowska, M

Adversarial robustness certification for Bayesian neural networks by Wicker, M, Patane, A, Laurenti, L, Kwiatkowska, M

Robustness guarantees for Bayesian inference with Gaussian processes by Cardelli, L, Kwiatkowska, M, Laurenti, L, Patane, A

Probabilistic safety for bayesian neural networks by Wicker, M, Laurenti, L, Patane, A, Kwiatkowska, M

Hardware-in-the-loop simulation and energy optimization of cardiac pacemakers by Barker, C, Kwiatkowska, M, Mereacre, A, Paoletti, N, Patanè, A

Individual fairness guarantees for neural networks by Benussi, E, Patane, A, Wicker, M, Laurenti, L, Kwiatkowska, M

When your fitness tracker betrays you: quantifying the predictability of biometric features across contexts by Eberz, S, Lovisotto, G, Patanè, A, Kwiatkowska, M, Lenders, V, Martinovic, I

Broken hearted: How to attack ECG biometrics by Eberz, S, Paoletti, N, Roeschlin, M, Kwiatkowska, M, Martinovic, I, Patané, A

Adversarial robustness guarantees for Gaussian processes by Patane, A, Blaas, A, Laurenti, L, Cardelli, L, Roberts, S, Kwiatkowska, M

Adversarial robustness guarantees for classification with Gaussian Processes by Blaas, A, Patane, A, Laurenti, L, Cardelli, L, Kwiatkowska, M, Roberts, S

Robustness of Bayesian neural networks to gradient-based attacks by Carbone, G, Wicker, M, Laurenti, L, Patane, A, Bortolussi, L, Sanguinetti, G

Probabilistic reach-avoid for Bayesian neural networks by Wicker, M, Laurenti, L, Patane, A, Paoletti, N, Abate, A, Kwiatkowska, M

Certification of iterative predictions in Bayesian neural networks by Wicker, M, Laurenti, L, Patane, A, Paoletti, N, Abate, A, Kwiatkowska, M

Statistical guarantees for the robustness of Bayesian neural networks by Cardelli, L, Kwiatkowska, M, Laurenti, L, Paoletti, N, Patane, A, Wicker, M

Bayesian inference with certifiable adversarial robustness by Wicker, M, Laurenti, L, Patane, A, Chen, Z, Zhang, Z, Kwiatkowska, M