On the adversarial robustness of Gaussian processes

On the adversarial robustness of Gaussian processes

<p>We study the robustness of Bayesian inference with Gaussian processes (GP) under adversarial attack settings. We begin by noticing how the distinction between model prediction and decision in Bayesian settings naturally leads us to give two different notions of adversarial robustness. The f...

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Bibliografiske detaljer
Hovedforfatter:	Patanè, A
Andre forfattere:	Kwiatkowska, M
Format:	Thesis
Sprog:	English
Udgivet:	2020

Lignende værker

Adversarial robustness guarantees for Gaussian processes
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Udgivet: (2022)

Adversarial robustness guarantees for classification with Gaussian Processes
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Udgivet: (2020)

Adversarially robust optimization with Gaussian processes
af: Bogunovic, I, et al.
Udgivet: (2021)

Adversarially robust optimization with Gaussian processes
af: Bogunovic, Ilija, et al.
Udgivet: (2022)

Robustness guarantees for Bayesian inference with Gaussian processes
af: Cardelli, L, et al.
Udgivet: (2019)

Multi-Class Triplet Loss With Gaussian Noise for Adversarial Robustness
af: Benjamin Appiah, et al.
Udgivet: (2020-01-01)

Adversarial robustness certification for Bayesian neural networks
af: Wicker, M, et al.
Udgivet: (2024)

Bayesian inference with certifiable adversarial robustness
af: Wicker, M, et al.
Udgivet: (2021)

Gaussian class-conditional simplex loss for accurate, adversarially robust deep classifier training
af: Arslan Ali, et al.
Udgivet: (2023-03-01)

A robust approach to Gaussian process implementation
af: J. Mukangango, et al.
Udgivet: (2024-10-01)

Gaussian processes with physiologically-inspired priors for physical arousal recognition
af: Ghiasi, S, et al.
Udgivet: (2020)

Adversarial attacks and adversarial robustness in computational pathology
af: Narmin Ghaffari Laleh, et al.
Udgivet: (2022-09-01)

Safety guarantees for iterative predictions with Gaussian Processes
af: Polymenakos, K, et al.
Udgivet: (2021)

Adversarial Gaussian Denoiser for Multiple-Level Image Denoising
af: Aamir Khan, et al.
Udgivet: (2021-04-01)

Using Gaussian Processes for Metamodeling in Robust Optimization Problems
af: Claudemir Mota da Cruz, et al.
Udgivet: (2023-12-01)

On the adversarial robustness of aerial detection
af: Yuwei Chen, et al.
Udgivet: (2024-11-01)

Adversarial Robustness with Partial Isometry
af: Loïc Shi-Garrier, et al.
Udgivet: (2024-01-01)

Adversarial robustness without perturbations
af: Rodríguez Muñoz, Adrán
Udgivet: (2024)

Understanding the landscape of adversarial robustness
af: Engstrom, Logan(Logan G.)
Udgivet: (2019)

ARGAN: Adversarially Robust Generative Adversarial Networks for Deep Neural Networks Against Adversarial Examples
af: Seok-Hwan Choi, et al.
Udgivet: (2022-01-01)

Physiologically-informed Gaussian processes for interpretable modelling of psycho-physiological states
af: Ghiasi, S, et al.
Udgivet: (2022)

Between-Class Adversarial Training for Improving Adversarial Robustness of Image Classification
af: Desheng Wang, et al.
Udgivet: (2023-03-01)

Biologically Inspired Mechanisms for Adversarial Robustness
af: Vuyyuru Reddy, Manish, et al.
Udgivet: (2020)

Adversarial robustness of deep reinforcement learning
af: Qu, Xinghua
Udgivet: (2022)

Adversarial robustness of Bayesian neural networks
af: Wicker, M
Udgivet: (2021)

Manifold-driven decomposition for adversarial robustness
af: Wenjia Zhang, et al.
Udgivet: (2024-01-01)

Stylized Pairing for Robust Adversarial Defense
af: Dejian Guan, et al.
Udgivet: (2022-09-01)

Hyperparameter Optimization in Generative Adversarial Networks (GANs) Using Gaussian AHP
af: Thiago Serafim Rodrigues, et al.
Udgivet: (2025-01-01)

Curb Detection and Mapping via Robust Iterative Gaussian Process Regression
af: Di Wang, et al.
Udgivet: (2024-06-01)

On the robustness of semantic segmentation models to adversarial attacks
af: Arnab, A, et al.
Udgivet: (2019)

On the robustness of semantic segmentation models to adversarial attacks
af: Arnab, A, et al.
Udgivet: (2018)

Settling the Robust Learnability of Mixtures of Gaussians
af: Liu, Allen, et al.
Udgivet: (2022)

Robustly Learning General Mixtures of Gaussians
af: Liu, Allen, et al.
Udgivet: (2023)

Adversarial Defense on Harmony: Reverse Attack for Robust AI Models Against Adversarial Attacks
af: Yebon Kim, et al.
Udgivet: (2024-01-01)

Adversarially robust property-preserving hash functions
af: LaVigne, Rio (Kristen Rio), et al.
Udgivet: (2021)

Robust decision trees against adversarial examples
af: Chen, H, et al.
Udgivet: (2021)

Robust decision trees against adversarial examples
af: Chen, Hongge, et al.
Udgivet: (2022)

Adversarially Robust Generalization Requires More Data
af: Schmidt, Ludwig, et al.
Udgivet: (2021)

On the adversarial robustness of Bayesian machine learning models
af: Blaas, AC
Udgivet: (2021)

Adversarial attacks and robustness for segment anything model
af: Liu, Shifei
Udgivet: (2024)